Continuous calibration of sensors in a remotely monitored cooling system

ABSTRACT

Systems, methods and apparatus may be applicable to managing and monitoring refrigeration assets, including refrigeration plants and cold-storage facilities comprising large numbers of refrigeration assets. A method of managing refrigeration systems includes receiving measurements captured by a plurality of sensors deployed with a refrigeration asset, the measurements being related to temperatures within a temperature-controlled chamber of the refrigeration asset, identifying a difference between a first temperature measurement obtained from measurements provided by a first sensor and a second temperature measurement obtained from measurements provided by at least one sensor, and calibrating the first sensor based on the difference between the first temperature measurement and the second temperature measurement.

PRIORITY

This application claims priority to and the benefit of U.S. ProvisionalPatent Application Ser. No. 62/902,849 filed in the U.S. Patent Officeon Sep. 19, 2019 and of U.S. Provisional Patent Application Ser. No.62/948,292 filed in the U.S. Patent Office on Dec. 15, 2019, the entirecontent of these applications being incorporated herein by reference asif fully set forth below in its entirety and for all applicablepurposes.

TECHNICAL FIELD

The present invention relates generally to management and calibration ofcooling systems, including HVAC, refrigeration and other environmentalcontrol systems.

BACKGROUND

Refrigeration cooling, cell-incubation and heating, ventilation, and airconditioning (HVAC) systems, collectively herein referred astemperature-controlled assets or assets, suffer a loss of operatingefficiency over time due to manufacturing defects, mechanicaldegradation, poor power-quality, adverse environmental factors, deferredmaintenance or simple misconfiguration. A loss of reliability orefficiency must be detectable, measurable and correctable, so as toavoid damage to equipment or spoilage to contents. Today however, thesystems and methods for determining the state-of-health for arefrigeration cooling, heating and HVAC system follow reactivefail-and-fix procedures, whereby repairs are applied only after theequipment fails—essentially, the failure of the asset is the firstevidence of a needed repair. This approach results in decreasingreliability over time for assets that have not yet failed, leading tothe highest possible labor and repair costs for assets when they fail interms of energy, repair and maintenance costs.

In Life Science and Pharmaceutical applications as just one example,high-reliability, uniformity and precision control of temperature isalso required to protect and assure the quality of research,manufacturing, transportation logistics or the storage of products andcommodities contained within a temperature-controlled asset, shippingcontainer or in a climate-controlled room. Products and commodities mustbe stored at prescribed temperatures which are often governed bygovernment regulation and subject to audit. Compliance requires that thestability and the uniformity of temperatures in the cabinet are verifiedwhich entails the gathering and analysis of temperature measurementsfrom multiple points, before products and commodities are added to theasset and, periodically thereafter according to standard operatingprocedures—typically every six (6) or twelve (12) months. The termapplied to the inspection protocols which demonstrate compliance iscalled validation.

The validation process is labor intensive and expensive, costing $2,000to $5,000 per asset or room and can take several days. Before thevalidation process can begin, the contents of the temperature chambermust be removed and to assess temperature stability and uniformity, anumber of sensors, sometimes a dozen or more are placed on or about theshelves or walls of the chamber or room to measure temperatures fromtop-to-bottom, side-to-side and front-to-back. After the validationprotocol is completed, the sensors are removed, and the asset is deemedto be validated if test protocol is completed and no deviations orexceptions are noted. The sensors and test equipment are then moved tothe next asset and the validation process is repeated. In a typical LifeScience facility with 300 Ultra-Low Temperature Freezers, the cost of avalidation project can exceed $600,000 per year. In addition to thecosts to administer a validation, the process can also induce andoperational issues due to scheduling delays, lack of skills or theavailability of specialized equipment.

Present industry best practices which rely on scheduled maintenance orvalidations are inefficient because an entire population ofrefrigeration assets must be inspected even though only a percentagemight require repair, maintenance or validation. Equipment malfunctions,mechanical degradation, deferred maintenance and environmentalconditions can cause temperatures to fall outside allowable limits(referenced by FDA as “exceptions”). When they occur,multi-million-dollar product losses are possible and are not uncommon.Continuous real-time monitoring systems are expensive and uncommon inthe industry today. To avoid the uncertainties of maintaining equipmentin a validated state, some operators adopt costly mitigation strategiesinvolving the replacement of refrigeration assets with a 10-year lifeafter only five years of service, even though there may be nothing wrongwith some refrigeration assets.

One example involves ultra-low temperature freezers (UL freezers or −80C freezers)—an estimated 500,000 of which are deployed in Life Scienceand Pharmaceutical manufacturing applications. Each ULT consumes theequivalent energy of an entire house, can account for 25-30% of allelectricity consumed within a Life Science facility, with 20-50% of theassets wasting an average of 20% of energy. More often than not, thiswaste is due to missing or inadequate diagnostics systems, and poorskills and repair techniques of the Service Provider. In addition towasted energy, the unexpected failure of a ULT Freezer in a Life Scienceapplication can result in the catastrophic loss of high-value, missioncritical research specimens or pharmaceuticals. Therefore, there is anongoing need for improved asset management systems applicable torefrigeration assets.

In another example, the contents of a refrigeration system in a LifeScience company possibly valued at several million dollars, may bedeemed spoiled (according to FDA regulations), and worthless if any partof the refrigeration compartment fails to maintain temperatures above orbelow a specific limit or standard. In some cases, the contents may alsobe deemed spoiled and worthless if the temperatures throughout thechamber cannot be verified over time, through well document measurementsusing calibrated instrumentation and precision test protocols.

In another example, cell-culture or production utilizes heatedincubators which maintain accurate temperatures, humidity and CO2concentrations. A lack of stability, uniformity or concentration in thechamber affecting any of these parameters can result in low productionyields or the complete loss of a growth cycle which sometimes takesmonths to compete.

Mission-critical cold-storage applications in the Life Science andPharmaceutical companies are a small part of the overall industry forrefrigeration which includes commercial refrigeration in food-processingfor example, and HVAC. The refrigeration and air conditioning repairindustry overall produces more than $2.5 billion in revenues annually,employs more than 38,000 repair technicians who continue to use “failand fix” repair methods that have been substantially unchanged for 50 ormore years. The refrigeration industry is also coming under increasedregulation by the governmental agencies through programs such as theEnergy Star™ program in the United States, which seek to drive adoptionnew energy conserving technologies into the laboratory equipment market.

SUMMARY

Certain aspects of the present disclosure provide improved assetmanagement systems and methods which employ proactive predict andprevent methods for anomaly detection. The methods disclosed can be usedfor dynamic recalibration of temperature sensors utilized to monitor thestability and uniformity of temperatures in an asset or room with morefrequency, at much reduced cost and with less labor. Certain aspects areapplicable to refrigeration systems and assets including individualrefrigeration assets, refrigeration farms comprising large numbers ofrefrigeration assets, and/or walk-in rooms which use one or morerefrigeration systems, which may be collectively referred to asrefrigeration systems or refrigeration assets.

In an aspect of the disclosure, a method of managing refrigerationsystems includes receiving measurements captured by a plurality ofsensors deployed with a refrigeration asset, the measurements beingrelated to temperatures within a temperature-controlled chamber of therefrigeration asset, identifying a difference between a firsttemperature cycle obtained from measurements provided by a first sensorunder test for accuracy, and a second temperature cycle obtained frommeasurements provided by at least one sensor, and calibrating the firstsensor based on the difference between the first temperature cycle andthe second temperature cycle.

In one aspect, the second temperature cycle is a baseline temperaturecycle obtained from measurements previously received from the firstsensor. In another aspect, the first sensor under test may reference asecond temperature cycle of at least one sensor associated with anothernominally operating asset such as an identical or comparable peer in apopulation, or against a peer model derived from an evaluation of one ormore comparable peers in a group or population.

In certain aspects, the second temperature cycle is obtained frommeasurements provided by two or more other sensors. The method mayinclude determining that the first sensor is out of calibration when themeasurements provided by the two or more sensors are consistent with oneanother and inconsistent with the measurements provided by the firstsensor.

In one aspect, identifying the difference between the first temperaturecycle and the second temperature cycle includes performing a frequencydomain analysis of the first temperature cycle and the secondtemperature cycle.

In one aspect, the method includes determining that the first sensor isout of calibration based on measurements of current consumed by therefrigeration asset.

In certain aspects, the method includes calibrating the plurality ofsensors prior to initial operation, detecting calibration errors,recalibrating the sensors based on differences in measurements providedby pairs or groups of sensors in the plurality of sensors after initialcalibration. The method may include recalibrating the differences inmeasurements provided by the pairs or groups of sensors after a changein conditions within the temperature-controlled chamber or room.Conditions within the temperature-controlled chamber or room may bechanged when an object is added to the temperature-controlled chamber orroom. Conditions within the temperature-controlled chamber or room maybe changed when an object is removed from the temperature-controlledchamber or room. Conditions within the temperature-controlled chamber orroom may be changed when an object is moved within thetemperature-controlled chamber, or if racking systems or shelves aremoved, realigned or changed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates one example of an apparatus that may be adaptedaccording to certain aspects disclosed herein.

FIG. 2 illustrates a system configured to monitor a plurality of assetsequipped with sensors in accordance with certain aspects disclosedherein.

FIG. 3 illustrates a first example of a refrigeration unit subject tomonitoring in accordance with certain aspects of this disclosure.

FIG. 4 illustrates an installation that includes multiple freezers.

FIG. 5 temperature profiles for refrigeration units.

FIG. 6 illustrates the condenser cycle within a temperature profile.

FIG. 7 illustrates a system for managing a network of refrigerationassets according to certain aspects disclosed herein.

FIG. 8 illustrates a refrigeration system instrumented with sensors andother monitoring circuitry according to certain aspects disclosedherein.

FIG. 9 illustrates a second example of a refrigeration unit subject tomonitoring in accordance with certain aspects disclosed herein.

FIG. 10 illustrates sensor layout within a temperature-controlled assetin accordance with certain aspects disclosed herein.

FIG. 11 illustrates a first example of a laminated sensor configured inaccordance with certain aspects of the disclosure.

FIG. 12 illustrates a second example of a laminated sensor configured inaccordance with certain aspects of the disclosure.

FIG. 13 illustrates a third example of a laminated sensor configured inaccordance with certain aspects of the disclosure.

FIG. 14 illustrates certain aspects of a system that can be configuredto calibrate and/or detect failure of sensors in accordance with certainaspects disclosed herein.

FIG. 15 illustrates an example of an architecture for a Mote configuredin accordance with certain aspects disclosed herein.

FIG. 16 is a data flow diagram that illustrates certain aspects ofsensor data processing and analysis using sensor streams in accordancewith certain aspects disclosed herein.

FIG. 17 illustrates an example of an apparatus employing a processingsystem that may be adapted according to certain aspects disclosedherein.

FIG. 18 is a flowchart illustrating a second example of a method ofcalibrating sensors in a refrigeration system.

FIG. 19 is a diagram illustrating an example of a hardwareimplementation for an apparatus adapted to calibrate sensors in arefrigeration system.

DETAILED DESCRIPTION

In the following description, specific details are given to provide athorough understanding of the various aspects of the disclosure.However, it will be understood by one of ordinary skill in the art thatthe aspects may be practiced without these specific details. Forexample, circuits may be shown in block diagrams in order to avoidobscuring the aspects in unnecessary detail. In other instances,well-known circuits, structures and techniques may not be shown indetail in order not to obscure the aspects of the disclosure.

Overview

Certain aspects of the present disclosure provide an improved assetmanagement system including new methods for anomaly detection to predictasset failures before they occur, new methods for continuous calibrationof temperature sensors at lower cost and with less labor, and methodsfor initiating inspection requests or repair orders based on detectingthe onset of equipment failure. Certain aspects are applicable tocontrolled-temperature refrigeration assets including individualrefrigeration assets, refrigeration farms comprising large numbers ofrefrigeration assets and refrigerated walk-in rooms; other aspects areapplicable to temperature-controlled heating assets such as cell-cultureincubators, space-heating and cooling systems such as HVAC systems whichprovide room heating and cooling. Each or all may be collectivelyreferred to as refrigeration assets or systems, heated or incubationassets, HVAC systems or collectively, temperature-controlled assets orassets.

In an aspect of the disclosure, one method includes determining bystatistical inference, machine learning or artificial intelligence, thatone or more sensors among the plurality of sensors distributed withinthe chamber of a temperature-controlled asset has experienced acalibration error or loss of accuracy due to drift, misplacement orcomplete failure, based on a determination that the sensor has lostcorrelation or covariance with its peers. For sensors which are deemedto be candidates for recalibration, the method of correcting(recalibration) in situ of each sensor, the amount of correction to beapplied and resultant limits of confidence may be determined frompreviously known correlations and confidence intervals of the sensor tobe recalibrated, in comparison to one or more sensors in the chamber orwith reference to sensors in a peer asset or in a group of comparableassets in a population. In some instances, the group of comparableassets include a physical asset, a simulated asset or a digital twin ofthe asset for which calibration is performed.

In an aspect of the disclosure, a plurality of sensors is distributedwithin the chamber of a temperature-controlled asset, providing themeans to immediately detect a change in the stability or distribution ofthermal energy (refrigeration) associated with the onset of equipmentfailure. A change may indicate a restriction of refrigerant or oil inthe capillaries or in a refrigeration circuit due to an accumulation ofdebris, or oil-logging the overcharging or undercharging of therefrigeration circuit, the use of an incorrect refrigerant formulation,a restriction of airflow around the asset or the presence of adjacentequipment or sources of heat which interfere with the cooling ability ofthe asset.

In another aspect of the disclosure, the method of detecting a seasonal(lead/lag) component in the correlations to improve the accuracy ofdetection models and to more closely analyze the reactions and responsesof the refrigeration or heating system to stress events such as dooropenings, power failures or changes to environmental factors.

In another aspect of the disclosure, upon detecting the onset ofequipment failure, the system may initiate or schedule an inspection orrepair event, providing information about which section of the chamberis affected.

In another aspect of the disclosure, analytics are derived from ananalysis of the stability, uniformity and time in or at temperature. Thecorresponding data may be used to calculate the Mean Kinetic Energy(MKT) of products stored in the chamber. In the event of a failure, thedata may be used to determine which products may have been affected by apower or equipment failure, based on a priori knowledge of the type ofand placement of product contained within the asset. Product losses canbe reduced with this information.

In an aspect of the disclosure, a method of managingtemperature-controlled assets includes receiving measurements capturedby a plurality of sensors. The measurements may be related totemperatures within a temperature-controlled chamber of the asset. Themethod may include identifying a difference in value, trend, pattern orcorrelation between a first temperature obtained from measurementsprovided by a first sensor, and a second temperature obtained frommeasurements provided by at least one other sensor. An evaluation of themeasurements may be time adjusted to account for detectable lead/lagrelationships among and between the sensors.

In one aspect, the second temperature is a baseline temperature obtainedfrom measurements previously received from the first sensor.

In certain aspects, the second temperature is obtained from measurementsprovided by at least one other sensors. The method may includedetermining that the first sensor is out of calibration when themeasurements provided by the other sensor or sensors are determined tobe consistent with each other but inconsistent with the historicallycorrelated measurements with the first sensor.

In one aspect, the difference between the first temperature and thesecond temperature may be identified. In one example, the difference naybe identified by performing a frequency domain analysis of the firsttemperature cycle and the second temperature cycle.

In one aspect, the method includes determining that the first sensor isout of calibration based on measurements of current consumed by therefrigeration asset.

In certain aspects, the method includes calibrating the plurality ofsensors prior to initial operation. Upon installing the sensors in or onan asset, the method may proceed by determining the relative ordifferences of values, trends, patterns or correlations among andbetween the sensors which provides an operational model for a correctlyoperating sensor located in or on a correctly operating asset. Thefailure or drift of one or more sensors may be detected as a change oruncorrelated difference of measurements from a sensor relative to atleast one other sensor in the plurality of sensors. The method mayinclude recalibration of at least one sensor based on knownrelationships and correlations of the sensor with at least one othersensor in the plurality of sensors. Conditions within thetemperature-controlled chamber may be changed when an object is added orremoved from the temperature-controlled chamber or following an accessevent which exposes the contents within the temperature-controlledchamber to the environment outside the chamber. Conditions within thetemperature-controlled chamber may be changed when an object, rack,shelf, box, vial or sensor is moved or replaced within thetemperature-controlled chamber, in response to a door-opening eventwhich introduces a warmer or colder thermal mass. Upon detecting theoccurrence of any such changes, the method may continue by decidingwhether a redetermination of the relationships and correlations of theplurality of sensors is required based on the presence and persistenceof the changes, and if so required, the relationships and correlationsmay be updated or redetermined.

In another aspect, the measured differences, correlations andtemperature trends among sensors over time may be used to generate abenchmark or score describing the reliability or ability of thetemperature-controlled asset to achieve or maintain a stable and uniformtemperature within the asset chamber following the addition or deletionof products and commodities or following door opening events. Thebenchmark or score may also enable a comparison of an asset against itsmake/model peers in a population to determine its relative performanceand whether repairs are necessary or (economically feasible) to restoreperformance to known achievable levels based on an analysis of its peersin a population of assets. Likewise, the benchmark or score can alsoenable comparisons between different makes or between different modelsfrom the same or different manufacturers for the purpose of makingintelligent asset purchase, retirement and repair decisions. Changes inbenchmarks or scores over time may indicate the onset of asset failuredue to the failure of insulation, control systems, mechanical failure orissues with power quality or environmental conditions.

In another aspect, refinements to the benchmark or scoring concept canbe derived from stress-EKG events, for example, when products are addedor removed from the chamber when the supply of power or phase-changematerial is interrupted—a score then indicating the time or ability ofthe asset to recover and provide a stabilized and uniform temperatureenvironment relative to its peers or other makes/models in a population.

In one aspect, a change in the correlation of a first sensor with atleast one other sensor can be attributed to detection of the failure ofthe first sensor.

In another aspect, the sensors have multiple installation options andconfigurations depending on the design of the compartment or room at thetime of manufacture or subsequently when installed as part of a fieldretrofit kit. The sensors can be installed within a compartment or roomand/or attached to the walls of the compartment or room, encapsulatedwithin, upon or about the shelves, racking systems, boxes or vials onshelves or retention systems within the chamber or room. Mats orpartitions that contain or encapsulate the sensors may be provided ordesigned to promote ease of installation or protection of the sensorsand cabling within the rack, shelf, chamber or room. The sensors maycommunicate with the monitoring system via wireless, wired, magnetic oracoustic means.

Various aspects of the disclosure relate to systems, apparatus andmethods that may be used to monitor, manage, control and report on theoperational reliability, temperature stability and uniformity ofrefrigeration systems that may be deployed locally or remotely and/or inlarge numbers. To facilitate description of certain aspects, specificdetails related to refrigeration and/or freezer assets will be given,and it will be understood that the aspects may be practiced withoutthese specific details. The concepts, methods, apparatus, and computerprogram products described herein relate equally to HVAC systems,environmental control systems, cooling systems, refrigeration systemsand associated refrigeration assets, including ULT refrigerators andfreezers, refrigeration plants, cold-rooms and cold-storage facilities.The performance of these various systems may be monitored, classifiedand correlated according to certain aspects of the disclosure and usingtemperature measurements, electrical current flow measurements,vibration measurements and/or other measurements that can be obtained.The measurements may have known, inferred, deemed, and/or calculatedcorrelation with refrigeration performance. Performance may quantifyand/or characterize the status, health, reliability temperaturestability, uniformity and/or energy usage of a refrigeration asset orrefrigeration system. Refrigeration assets in need of repair may beidentified and a repair process may be specified, classified, managedlocally and/or remotely.

Certain aspects of the disclosure relate to the management, calibration,documentation and validation of performance over time of refrigerationcooling (refrigeration) and heating, HVAC systems, which may be referredto collectively or individually as “environmental systems,” or systems,herein. Environmental systems may employ electrical, mechanical,electro-mechanical, Peltier, evaporative and phase-change materials suchas liquid nitrogen and dry-ice as a refrigeration or heating sources.Environmental systems are often deployed in retail and commercialapplications but for some applications, such as life science research,pharmaceutical manufacturing and other scientific and food-relatedapplications. The systems and environmental chambers in particular,perform mission critical cold-storage functions which require stable andprecise temperatures and accurate measurement instruments to assess andassure their performance.

It is necessary that a temperature-controlled system be able maintain acontinuously stable temperature at multiple points inside the systemchamber, and in some mission critical applications, to a specified levelof accuracy using sensors which are calibrated using specializedcalibration reference instruments. The use of the instruments requiresmanual labor to install and remove the many sensors which are requiredbut most often not built into the system, in order to derive atemperature map of all useable areas and containment spaces inside thechamber or a system. Because sensors can lose accuracy or become damagedover time, temperature mapping or validation is performed at periodicintervals, such as at every six-months, one-year, two-years etc., whichis labor intensive, often costing thousands of dollars per mappingoperation for each freezer. In some manufacturing operations,particularly in the Life Science and Pharmaceutical manufacturingindustry, the temperature mapping process is highly controlled anddocumented referencing standard operating procedures published by themanufacturer or by industry organizations or government bodies.Conventional regulations and specifications require labor for thetesting and labor to prepare the validation documentation, thedocumentation is subject to audit by the standards body. At the end ofthe validation process, the system is deemed to pass or fail. If itfails, a repair is indicated, if system fails again after repair, thesystem cannot be put to use for any purpose which requires conformanceto a validation specification, but it may be suitable for other lesscritical applications. Sometimes the system is replaced because itcannot be repaired to meet a standard or validated level of performanceaccording to validation criteria or standard operating procedures.

Certain aspects of the present disclosure may be described in relationto a variety of types of refrigeration assets, including refrigerationfarms comprising large numbers of refrigeration assets. Systems andmethods are described that may be used to monitor and analyzeperformance of HVAC and/or refrigeration assets, and can identify andselect refrigeration assets in need of repair. In some aspects, theperformance of sensors and other equipment used to monitor and analyzeperformance of HVAC and/or refrigeration assets may also be ascertainedthrough the analysis of measurements delivered by the monitoringequipment.

Certain aspects of the present disclosure provide improved management ofrefrigeration systems based on proactive predict-and-prevent methods foranomaly detection, and improved methods utilizing less labor forrecalibration and revalidation of assets, and detection of temperaturesensor calibration errors. Certain aspects are applicable torefrigeration and HVAC systems and assets including individualrefrigeration assets, refrigeration farms or biorepositories comprisinglarge numbers of refrigeration assets, and/or walk-in rooms which useone or more refrigeration systems, which may be collectively referred toas refrigeration systems or refrigeration assets.

Certain systems and methods are provided that can determine the statusand/or state-of-health of HVAC, refrigeration, assets and monitoringequipment, where monitoring equipment may include wired and/or wirelesssensors that transmit data to an application server for analysis andbenchmarking of performance. Data may be processed and measured againsttime or in reference to predefined benchmarks and/or norms in order todetermine relative performance in reference to selected peers as definedby query criteria, normalization, lead/lag adjustments to the timeseries or filters. The analysis and results may be represented with avisual indication, mathematical or pattern recognition function, such asa sine wave or a statistical model. The application server may beaccessed through any application, web browser or web interface, and theuser can have a distinct login identification and password which definesroles and privileges for access and utilization of the interface.

Systems, methods and apparatus may be applicable to managing the healthand determining the validation status of refrigeration assets bymeasuring, mapping, and comparing the sensor temperature measurementswith reference to a specification or industry standard, thedistribution, uniformity and stability of temperatures in refrigerationassets, including, refrigeration plants, cold-storage facilitiescomprising large numbers of refrigeration assets, andtemperature-controlled shipping containers. Refrigeration systems may bemanaged by receiving measurements captured by a plurality of sensorsdeployed within a refrigeration asset, the measurements being related totemperatures and temperature cycles within a chamber, identifying adifference between measurements of temperature obtained from a firstsensor occurring during a temperature cycle and temperature measurementsfrom at least one other sensor occurring during its temperature cycleand calibrating the first sensor based on the difference between thetemperatures of the first and at least one other temperature sensor. Thetemperature measurements differences may be adjusted for a time delay,cycle variance or offset due to the differences of the location orthermal mass near or surrounding one or more sensors. A statistical orfrequency domain analysis of a first temperature cycle and a secondtemperature cycle may be used to adjust for time-delay differencesattributable to design of the refrigeration system, probe placement ordue to the commodities or structures placed near, around or attached tothe sensors.

FIG. 1 illustrates an example of an apparatus 100 that includes ormanages sensing devices 106. The apparatus 100 may include a processingcircuit 102 that has multiple subcomponents or devices 104, 106, 108,122, 122, 126. In some instances, the processing circuit 102 may beimplemented in an SoC. In some instances, the processing circuit 102 maybe implemented in, or include an ASIC. In one example, the apparatus 100may include an RF transceiver 108 that enables the apparatus 100 tocommunicate through one or more antennas 124 with a radio accessnetwork, a core access network, the Internet and/or another network.

In the example illustrated in FIG. 1, the processing circuit 102includes an ASIC device 104 that has one or more processors 112, andother logic circuits. The processing circuit 102 may be controlled by anoperating system and may provide an application programming interface(API) layer that enables the one or more processors 112 to executesoftware modules residing in a storage device 114, 122 for example. Thesoftware modules may include instructions and data. The ASIC device 104may access its internal storage device 114, external storage 122, and/orother storage devices. The storage devices 114, 122 may includeread-only memory (ROM) or random-access memory (RAM), electricallyerasable programmable read-only memory (EEPROM), flash cards, or anymemory device that can be used in processing systems and computingplatforms. The processing circuit 120 may include, or have access to alocal database or other parameter storage that can maintain operationalparameters and other information used to configure and operate theapparatus 100 and/or the processing circuit 102. The local database maybe implemented using registers, a database module, flash memory,magnetic media, EEPROM, optical media, tape, soft or hard disk, or thelike.

The processing circuit 102 may communicate through one or more interfacecircuits such as the RF transceiver 108, which may include a combinationof circuits, counters, timers, control logic and other configurablecircuits or modules. In one example, the RF transceiver 108 may beconfigured to operate in accordance with standards-defined communicationspecifications or protocols. The processing circuit 102 may include orcontrol a battery or power management device 126.

FIG. 2 illustrates a network of devices 200 that may be deployed tomonitor various types of refrigeration system, HVAC system or otherenvironmental control systems. A plurality of networked devices 202a-202 n, 204 a, 204 b, each of which may be referred to as a “Mote,” maybe adapted or configured to sample data produced by one or more sensors,and to transmit the sensor data to a mobile computing device 208 orprocessing devices 220. In one example, one or more of the networkeddevices 202 a-202 n, 204 a, 204 b may be implemented using an apparatus100 as illustrated in FIG. 1. In one example, each processing device 220may perform certain functions as part of a systems that includes thenetwork of devices 200. Each processing device 220 may be accessiblethrough a network 210 which may include the Internet. In anotherexample, the mobile computing device 208 may be configured to enablefield service personnel to interact with equipment targeted for servicecalls and with other portions of a system that includes the network ofdevices 200. The mobile computing device 208 may include one or morewired or wireless transceivers and/or line drivers and receivers thatenable the mobile computing device 208 to communicate with certain ofthe networked devices 202 a-202 n, 204 a, 204 b, and/or other processingdevices 220 coupled to the network 210. In some instances, the mobilecomputing device 208 includes or may be coupled to one or more externalsensors that can be used to monitor an asset during field servicing. Insome instances, the mobile computing device 208 may interface with acomputing system or other intelligent device provided within a managedasset.

The mobile computing device 208 or processing devices 220 may includerespective processing circuits 212 adapted or configured to communicatewith and/or control with the networked devices 202 a-202 n, 204 a, 204b. In one example, a processing circuit 212 includes circuits and/ormodules 214 configured to receive and process sensor data sampled by theby the networked devices 202 a-202 n, 204 a, 204 b, circuits and/ormodules 216 configured to process the sensor data to derive sensormetrics used for determining health of assets and changes or differencesin health of an asset with respect to peer assets and/or relative toprior states of the asset, and circuits and/or modules 218 configured tomanage or monitor operational characteristics of sensing devices 106.

In the illustrated example, some the networked devices 202 a-202 n maycommunicate through a network 210 using wired or wireless communicationstechnology, while other networked devices 204 a, 204 b may be coupled toan aggregator 206 that collects, processes and/or forwards sensor datafrom the networked devices 204 a, 204 b. Each of the networked devices202 a-202 n, 204 a, 204 b may sample data from one or more sensors.

Certain aspects of this disclosure relate to systems used to store,maintain and manage materials at ultra-low temperatures. In one aspect,cooling systems used to store specimens, pharmaceuticals and other lifescience products, by-products and source materials can be monitored inreal-time using sensors and instruments that are maintained incalibration by continuously monitoring and validation of the sensors.Validation may describe or relate to a process or test procedures forassessing the stability and uniformity of temperatures in the chamber ofa refrigeration asset. In one example, testing protocols may be providedby third parties such as the National Institute of Standards andTechnology (NIST), a test authority such as Underwriters Laboratories(UL), or an industry association such as College of AmericanPathologists (CAP) or World Health Organization (WHO). In some examples,the cooling system may include a biorepository that employs one or moreULT refrigeration systems to store specimens used in Life Scienceapplications.

In many instances, a biorepository is certified and/or accredited by anorganization such as the College of American Pathologists in the UnitedStates of America. To be certified or accredited, the biorepository maybe required to comply with certain standards governing and proceduresdetailing storage conditions for specimens of different types, and forcalibration of storage equipment and sensors used to monitor storageequipment. For example, the standards may relate to the operating rangeof ULT refrigeration systems, defining limits, tolerances and/ortemperature set points that define operating temperature range instorage compartments of the ULT refrigeration systems.

An operator may be required to record or log temperatures observedwithin the storage compartments of the ULT refrigeration systems. In oneexample, the operator may be required to log the average temperature,maximum, and/or minimum temperature within a 24-hour period may belogged. A certification entity may define standards for calibration ofsensors. The calibration standards may define a periodicity forcalibration or recalibration of sensors that measure temperatures in thestorage compartments of the ULT refrigeration systems. In someinstances, sensors must be initially calibrated using astandards-certified thermometric device. In some instances, there may bea recalibration that temperature sensors be periodically recalibratedaccording to a defined schedule or within a defined period.

Certain aspects disclosed herein enable a cooling system to be remotelymonitored such that temperature measurements within cooled storagecompartments can captured continuously and logged at any desired orrequired frequency. Certain aspects of the disclosure enable continuouscalibration of sensors in remotely monitored cooling system without theuse of external equipment. In some instances, a remote monitoring systemreceives temperature information from sensors in the cooling system,where the temperature information is processed to capture temperaturevariations and trends. In one example, temperature variations can becaptured with a 1-minute resolution. The automated system operatescontinuously and can provide alarms, alerts and other notifications on a24-hour basis. The alarms, alerts and other notifications permit rapidresponse to fast-onset critical issues, and may permit preemptive actionto avoid occurrence of such issues.

FIG. 3 illustrates an example of a freezer 300 in a cooling system thathas been implemented in accordance with certain aspects disclosedherein. The freezer 300 may be instrumented using sensors that can beremotely monitored. The freezer 300 has an internal compartment 308 thatis maintained at a desired temperature by a refrigeration device 306.For example, the refrigeration device 306 may operate using athermodynamic refrigeration cycle such as the Carnot cycle and maycomprise one or more compressors, condensers, evaporators and a chargeof a refrigerant, such as Freon. It will be appreciated that athermodynamic refrigeration cycle can be implemented using arefrigerator and/or using a heat pump. Certain aspects of the presentinvention may be applied to heating and cooling systems. Certain aspectsdisclosed herein may be described or illustrated using the example of afreezer 300 or refrigeration device 306 which accomplishes deep coolingof a compartment 308 used to store items and materials which mayinclude, for example, biological samples. It will be appreciated thatthe invention may be used in commercial food storage systems, consumerequipment and any other refrigeration system or system that uses a heatpump. In one example, an optimal and/or desired practical operatingtemperature for a ULT freezer is approximately −80° Celsius. Theselection of operating temperature is typically made based onapplication requirements and operational limits of the refrigerant orother component of a system or freezer 300. In the example of a ULTFreezer using a Freon refrigerant, the Freon-based thermodynamicrefrigeration cycle typically has a lower operating temperature limit of−86° Celsius.

It will be appreciated that the refrigeration device 306 may includemultiple component parts, some of which may be installed within and/oradjacent to compartment 308. Certain embodiments employ thermal sensors302 that are in thermal communication with the storage compartment 308to measure temperature within a compartment 308 of freezer 300. In someinstances, electrical sensors may measure power consumption, voltagesand/or currents in the refrigeration device 306. In some instances,vibration sensors may be mechanically coupled to mechanical and/orelectromechanical components in the refrigeration device 306. In someinstances, environmental sensors may provide information related to theenvironment in which the freezer 300 is located.

Sensors can be electrically or electromagnetically coupled to acontroller 304 (e.g., a Mote), which may be located outside thecompartment 308 or cooled interior of a refrigeration system or freezer300. Location may be selected to ensure an ambient temperature forelectronics components that is within operational tolerances ofelectronics devices. Certain aspects disclosed herein may employ sensorsinstalled in HVAC systems, refrigerators and freezers during manufactureor assembly.

Location of the controller 304 may be selected to facilitate access topower, for optimal wireless communication and/or for easy access duringsetup and maintenance. The controller 304 may include one or moreprocessors, storage and means of communications as will be addressed inmore detail below. Placement of the controller 304 on the outside of thefreezer serves to ensure any contents inside the compartment 308 willnot damage the sensor 302 or inhibit transmission of the readings. Insome embodiments, the controller 304 may comprise a wirelesstransmitter. In some instances, sensors and/or controllers 304 mayemploy hard-wired or other communication means instead of, or inaddition to, a wireless transmitter. A controller 304 can be powered byinternal battery and/or from an external power source or supply.

In certain implementations, the controller 304 may transmit accumulatedvibration, temperature, current, voltage and/or power measurements overa predefined time interval. It is contemplated that some controllers 304may be configured to relay measurements without processing and/or mayrelay temperature measurements when contacted by another controller (notshown), gateway and/or accumulation device, regardless of the amount ofdata accumulated by the controller 304.

Turning to FIG. 4, an installation 400 may include multiple freezers 412₁-412 ₁₀ in various rooms or structures 402, 408, which may also includeenvironmental control plant 404, 410. The environmental control plant404, 410 may be an HVAC system or a refrigeration system. In someimplementations, individual freezers 412 ₁-412 ₁₀ can monitor and trackthe location of objects within the rooms or structures 402, 408. Eachfreezer 412 ₁-412 ₁₀ can be assigned a distinct icon, relevantidentifying information, and strategically incorporated in a virtuallayer of a representation of the installation 400. In therepresentation, a distinct icon associated with each freezer 412 ₁-412₁₀ can indicate an alarm status by its changed color or an action, suchas a spinning or revolving motion. For example, a freezer 412 ₁-412 ₁₀with an alarm status may be represented by a red icon and/or a spinningicon. In certain implementations, animation of an icon may correlate toa specific event or type of alarm. For example, a rapidly spinning iconcan represent a freezer 412 ₁-412 ₁₀ with an observed increase in energyconsumption. A red icon may represent a freezer 412 ₁-412 ₁₀ that hasbeen improperly (re)located within the installation 400. A blinking iconcan represent a freezer while it is being moved. Icons may be assignedto the environmental control plant 404, 410.

For each freezer 412 ₁-412 ₁₀ in the example, a sensor 414 such as amagnetometer, global positioning satellite (GPS) device 418, or othertype of device may be placed near the freezer door 416. The sensor 414may indicate position of a freezer 412 ₁-412 ₁₀ and/or relative positionof the freezer door 416. A current transducer, voltage sensor orvibration sensor may also be monitored. The information from the varioussensors can be used to determine operational characteristics, locationand orientation of the freezer relative to a wall or structure andmonitor when a freezer is moved from one location to another. Readingsfrom the magnetometer can also indicate when the door is open and canaccount for irregularities in temperature data. Energy is added to aconditioned area when the door is opened and creates changes that arenot indicative of compressor performance. In addition to a temperaturesensor, integrated transceiver, and current transducer, a comprehensivemonitoring unit may also include additional attached digital inputdevices, such as an attached humidity sensor.

FIG. 5 illustrates examples 500 of profiles generated from temperaturemeasurements for one type of refrigeration unit. These profiles aretypically generated from temperature data captured over a period oftime, and analysis of the temperature data can be used to determinefreezer performance over the time period. Baseline profiles can beobtained as an aggregate of profiles of a plurality of similar freezersand/or from a freezer that is functioning within rated specifications.In particular, temperature cycles within the compartment can becharacterized using statistical and pattern analysis techniques toderive a steady-state characteristic of temperature within thecompartment.

A baseline for a population of freezers of various manufacture andmodels can be established by comparing the plotted curves of each unitto one another in order to establish a normalized average pattern. Abaseline can also be established using previously captured data obtainedfrom a similar model freezer or group of freezers known to be running atnominal and/or optimal performance levels. Baseline data can be providedby a user and/or by download from a networked repository of baselines.Baseline data can be used for comparison with a temperature profilecaptured from an individual unit and the comparison may then yieldinformation correlated to the health of the individual unit. Manyapproaches in establishing a baseline are conceivable, and some of theseapproaches may be user-defined. In certain embodiments, any appropriatecomparison metric may be used. In one example, the comparison metric maycomprise a statistical moving average.

In FIG. 5, an idealized and simplified baseline profile 504 of a freezeroperating at optimal conditions is generally sinusoidal. The sine curveoscillates about −80° Celsius and has a consistent amplitude withminimum and maximum values of −84° Celsius and −76° Celsius,respectively. In this example, the refrigeration cycle may be consideredto be active while the internal freezer temperature is greater than −80°Celsius and is disabled when the temperature is at, or below the nominaloperating temperature of −80° Celsius. Because of thermal inertia withinthe freezer, temperature continues to change after the refrigerationcycle transitions between on and off. It will be appreciated that thevolume and mobility of air within the freezer typically dictates thefrequency of oscillation of temperature. Accordingly, the sample rate oftemperature may be adjusted based on frequency of oscillation.Typically, at least two temperature readings per time period is requiredto capture frequency of oscillation, although more samples are generallyrequired to identify the form of the curve, harmonic frequency contentand step functions attributable to events, malfunction, etc.

Variations of maximum, minimum and median temperatures from nominalvalues, and/or shape of sections of the temperature characteristic canbe used to assess the efficiency of the thermodynamic refrigerationcycle and to indicate potential operational deficiencies of therefrigeration equipment. Temperature measurements can be analyzed andcompare the freezer's performance to a baseline or stages duringoperation. Analysis of continuous temperature readings taken fromindividual freezers or groups of freezers can provide data or a patternof temperature profiles that can be used to optimize power usage,schedule maintenance visits and generate alarms.

Signal analysis can be performed on a temperature profile using, forexample, a statistical model. For example, a profile may indicate a peakrepresenting a maximum temperature that terminates a rise from minimumto maximum values. The peak may be calculated as a statistical maximum.The profile may also indicate a minimum temperature terminating a fallin temperature as compartmental temperature is regulated. The minimumtemperature may be calculated as a statistical minimum. In one example,the statistical maximum and minimum values may be calculated using amoving average. A statistical model can distinguish a true maximumand/or minimum temperature from other peaks and troughs in temperatureidentified within the profile. It will be appreciated that a variety ofdata filtering and analysis tools are known that can be applied toextract statistically significant characteristics of the profile. Suchstatistically significant characteristics can be used to manage freezerhealth.

The characteristics of a unit's temperature profile can be used todiagnose potential problems with refrigeration and other devices and canalso be used to identify failing components. Advantageously, data can beobtained using wireless sensors from one or more refrigeration devicesand the data can be characterized and categorized according to knowncorrelations. As discussed above, the example of FIG. 5 provides abaseline profile 504 representing a freezer operating at optimalconditions. FIG. 5 also shows a profile 506 that is a simplified exampleof compressor saturation when temperature within the freezer reaches theoperational limit 508 of Freon at −86° Celsius. It will be appreciatedthat profile 506 most likely represents a system in which the thermostatis set just above −86° Celsius and that the compressor continues tooperate without effect until temperature within the freezer stabilizesat −86° Celsius or the temperature surrounding the thermostat reachesthe set desired temperature. The system represented by profile 506 isoperating inefficiently because the compressor operates withoutproducing useful work.

A profile 502 with a higher frequency, more rapid refrigeration cycles,in comparison to the baseline profile 504, might be attributed to anincorrectly calibrated freezer relative to its storage capacity.Observed temperatures that fall outside a range of internally calibratedtemperature ranges may indicate one or more problems including, forexample, a Freon leak through a valve or through a deteriorating seal,lack of Freon circulation due to a clogged capillary tube, aninefficient or damaged condenser fan, a clogged filter, obstructedcondenser coils, worn compressor valves, and/or other mechanicalfailure. A greater amplitude of oscillation during a compressor cyclemay indicate a transfer of energy between the freezer and thesurroundings in the form of heat resulting in increased power usage. Arise in temperature results in more current being drawn through theunit. An irregular spike in temperature can indicate damaged insulationwithin the compressor coil. As discussed herein, deviations from anormal or ideal temperature characteristic can be correlated withdysfunctions of the cooling system, inadequate air circulation withinthe compartment of a refrigerator or freezer. Correlations ofdysfunction with observed temperature cycles are enabled in certainembodiments of the invention by observing, recording and analyzingtemperature variations in a plurality of refrigeration devices overprolonged periods of time.

FIG. 6 illustrates an example of a temperature characteristic 600 of afreezer compartment monitored according to certain aspects of theinvention. The characteristic 600 identifies certain inferences that canbe drawn regarding condenser or compressor cycles 610. As depicted, thecharacteristic 600 is substantially sinusoidal having a phase shiftedhalf-cycle 604 that corresponds to a period when the condenser of thefreezer is active, followed by a half-cycle 606 when the condenser isinactive. Condensers function to cool the refrigerant, such as Freon,and convert the vapor into a liquid while at a constant temperature andpressure.

The phase shift between temperature characteristic 600 and the condenserhalf-cycles 604 and 606 can arises because of lags 602 and 608 inmeasured temperature response to compressor transitions between activeand inactive, air flow within compartment 308 and for other reasons. Intypical applications, condenser half-cycles 604 and 606 may be affectedby other stages and characteristics of a refrigeration cycle, such asflash point evaporation and movement of the refrigerant through theevaporator. Transition between condenser half-cycles 604 and 606 may becorrelated with a point in time before the highest temperature observedand corresponding to the crossing of an upper temperature threshold 612.Transition between condenser half-cycle 604 and next compressor activehalf-cycle 606 may be correlated with a point in time before the lowesttemperature observed and corresponding to the crossing of a lowertemperature threshold 614.

In certain embodiments, compressor activity can be inferred from thetemperature characteristic 600. In the example illustrated in FIG. 6,the temperature characteristic 600 is symmetric and phase shifts or lags602 and 608 can be assumed to have an equal length of time and can beeffectively ignored. Therefore, it can be inferred that the condenser isturned on for 50% of the time. In other instances, the ratio of activeand inactive condenser periods may be non-unitary and the resultingtemperature characteristic is likely to non-sinusoidal but is likely tobe periodic. Accordingly, signal analysis techniques may be employed insome embodiments to extract a fundamental frequency of the refrigerationcycle and to identify other cycles that can be attributed to condenseractivity and inactivity. In some embodiments, analysis of thetemperature characteristic may identify acceleration/deceleration ofrate of change of temperature within a half cycle of the temperaturecharacteristic; these changes in acceleration and/or deceleration, andgeneral trends in the temperature characteristic before such changes canbe used to identify compressor on/off transitions. Changes and trendsmay also be determined from current, voltage, power consumption andvibration measurements.

Other functions of a refrigerator may be identified from sensormeasurements. For example, changes in the characteristic and/or thepresence of certain harmonic frequencies may indicate when a pump isengaged, when refrigerant vapor is compressed and/or superheated. Longterm changes in an observed temperature characteristic can be predictiveof refrigeration system failure or loss of energy efficiency. Forexample, progressive deterioration of mechanical parts can lead tochanges in the temperature characteristic (e.g. increased cyclefrequency) that are detectable, given sufficient lapsed time. The rateof change and the manner of change can indicate impending loss ofefficiency or failure based on similar changes previously observed otherrefrigeration devices.

It is also contemplated that the relationship between multiple connectedcompressors within a freezer can be identified by analysis of atemperature characteristic. In one example, the high-pressure outputstage of a first compressor can be coupled to the low-pressure inputstage of a second compressor. In another example, refrigeration systemsmay operate in parallel. Other refrigeration component combinations arecontemplated. The relationship between the coupled compressors can beidentified in a single characteristic obtained from sensors, while thestate of health of each individual compressor in the combination may beinferred. As discussed herein, the cooling devices of some commercialrefrigerators and freezers are instrumented such that direct readingsobtained from compressors, evaporators and Freon lines may be used toaugment temperature readings obtained from within the compartment 308.Such direct readings can be useful in isolating specific components inpredictive failure analyses. Certain embodiments develop informationregarding relationships between direct readings and measured compartmenttemperatures; such relationships may be used to identify problems withheat transfer elements and airflow with a compartment 308.

The attribution of potential problems to specific refrigeration unitsand/or component can be achieved in certain embodiments of theinvention. In particular, the use of indirect temperature and/orelectrical current measurements can determine the financial cost andbenefit associated with each refrigeration unit. Measurement ofelectrical current can supplement cost/benefit analysis. For example,condenser activity may be accurately characterized for a plurality offreezers and such characterization may be used to estimate power usage,based on rated current usage of the condensers. However, amalfunctioning or laboring condenser may consume considerably more powerthan a nominally operating condenser. Therefore, in certain embodiments,the monitoring of a population of refrigeration/freezer devices caninclude measurement or estimation of electrical current usage and theeffects of dysfunctional refrigeration components on power consumption.Information developed from such monitoring can be used to correlateelectrical current readings to power consumption of condensers. Forexample, information from monitoring can be used to determine the amountof electrical current drawn by a condenser and the temperaturecharacteristic for the refrigerator can be used to determine the amountof time that the condenser is active.

Electrical current drawn by the condensers can be characterized for eachindividual freezer, for makes and models of freezer and can electricalcurrent can be measured on a continuous or periodic basis. In certainembodiments, current measurements can be obtained for a freezer orrefrigerator that is operating at designed efficiencies. Thesemeasurements can be correlated with temperature measurements for therefrigerator and freezer and can serve as a baseline for monitoringenergy efficiency and for predicting failure.

Electrical current usage may be used to calculate power consumption bythe refrigeration unit. The active power P required by a refrigerationunit operated at V volts and drawing I amps, is calculated as P=IV.Power used is then calculated as PT_(ON) Watt Hours (typically stated askilowatt hours), where T_(ON) is the fraction of each temperature cyclewhen the compressor is active and drawing current. It will beappreciated that actual power usage may be obtained by scaling activepower P with known power factors of the compressor or compressor type.Thus, calculation of power consumption by a refrigeration unit requiresknowledge of current used by the refrigeration unit.

In certain embodiments, current load for an individual refrigerationunit may be determined or inferred, given that the power cyclingproperties of the refrigeration unit are predictable and/ordeterminable. In certain embodiments, electrical current measurementscan be obtained for each refrigeration unit. In certain embodiments,current is measured at a power distribution panel, or on a power linethat supplies current to multiple refrigeration units. Baselinemeasurements of electrical current drawn through a circuit supplying twoor more refrigeration units or freezers can be used to determine theload of each refrigeration unit. Given sufficient time and data points,a periodic measurement of current in a circuit that feeds pluralrefrigeration units can provide sufficient information to discern thecontributions of current use by each of the refrigeration units.Typically, variations in current measured at the power source can becorrelated with temperature characteristics measured simultaneously ateach of the freezers, thereby attributing current usage by therefrigeration unit.

Temperature and current correlations may be determined using a varietyof techniques. In a simple example, when a temperature characteristicshows that a selected refrigeration unit is in a warming phase, it canbe assumed that the selected refrigeration unit is drawing no current.When the selected refrigeration unit begins a cooling phase, anyincrease in measured electrical current may attributed to the selectedrefrigeration unit, if no other refrigeration unit has transitioned from“on to off” or from “off to on.” Given sufficient data points, theindividual contribution of each refrigeration unit can be calculatedbased on identified combinations of “on and off” refrigeration units. Itis frequently more convenient to perform a frequency domain analysis ofmeasured electrical current flow to find correlations with temperaturecycles of refrigeration units that receive the electrical current. In asimple example, if four freezers are provided electrical current fromthe same power outlet, and the compressors of the refrigeration unitsoperate at different frequencies, it would be sufficient to use a notchfilter tuned to the frequency of a selected refrigeration unit tomeasure the amplitude of current attributable to the selectedrefrigeration unit. However, current and temperature data are typicallyanalyzed using well-known signal analysis techniques.

Attribution of current consumption by individual refrigeration units mayinclude the use of various algorithms that identify variations incurrent usage over a refrigeration cycle and can provide an averageelectrical current flow and percentage “on” time for each compressor.Having determined electrical current usage and “on-time” for eachrefrigeration unit, a user can determine power usage for eachrefrigeration unit over time, in comparison to other systems, and incomparison, to benchmarks and/or nominal operating specifications. Thus,a single electrical current measurement device can provide insight intothe efficiency of multiple systems. Indirect estimation of electricalcurrent load drawn through an individual unit can also allow for ameasurement of efficiency based on energy consumption when compared witha healthy unit.

In certain implementations, methods for calculating power usage includemeasuring electrical current in a power line that supplies a pluralityof refrigeration units. Electrical current measurements are typicallysampled at a sufficient rate to enable correlation of variations inelectrical current flow with the characteristics of the individualrefrigeration units. A processor or controller can be configured toidentify when each compressor is active, typically by analyzing thetemperature characteristic for the refrigeration unit. At various samplepoints in time, a list of active compressors can be associated with acurrent measurement in the common power supply. With sufficient samples,the contribution of electrical current by each compressor can becalculated. This contribution represents electrical current consumed bythe compressor and can be used to calculate the power consumption of therefrigeration unit based on the ratio of compressor active/inactiveperiod. Accordingly, the efficiency of each refrigeration unit can bedynamically monitored.

In certain embodiments, a temperature sensor can recognize eventsrelating to changes in temperature and respond by increasing samplerate. An application server may be configured to treat such increasedsample rate as an alert for the corresponding refrigeration unit. It iscontemplated that an alert may be triggered by any of a plurality ofevents, including detection of compartment temperature rising above oneor more thresholds, temperature remaining above a maximum threshold fora predetermined amount of time, changes in rate of change of temperatureand step changes in temperature. Similarly, alerts may be generatedbased on abnormal or sudden increase in current consumption. An alertrelated to temperature rising above a threshold can often be attributedto the door being opened. If the temperature readings remain at orexceed the maximum temperature, a door of the refrigeration unit mayhave been left open, and/or complete compressor failure may haveoccurred. Events may also include temperature range shifts fromstatistical norms for the refrigeration unit, often attributable to afailing or laboring compressor, incorrect calibration and/or changes inenvironmental conditions. For example, a compartment of a refrigerationunit may be overloaded given its storage capacity, and therefore unableto maintain a steady temperature. Alerts are typically sent to a user byEmail, text message automated call, notification on a Web page and/or byany other suitable means.

Environmental changes may affect temperature readings and can includethe specific placement of a refrigeration unit within a room adjacent toan unaccounted for heat sink. Refrigeration units located near or by awindow may display increases in temperature that correlate to atemperature increase due to time of day and/or seasonal weather. Thetemperature of the room or structure in which the refrigeration unit ishoused may increase due to improperly functioning or set environmentalcontrols. An increase in temperature readings can also correlate to anincrease in current or power usage, as the compressor does more work tomaintain compartment temperature. In certain embodiments, a collectiveefficiency metric can be calculated as power usage per area occupied byrefrigeration units. One such metric can be calculated as kWh/sq. ft.and the metric may be used to assess relative efficiency betweendifferent makes and models of refrigeration units. The metric may alsobe used to set thresholds for performance such that a decrease inperformance may trigger an alert, generate a service request, etc.

In certain embodiments, portions of data collected by temperature and/orelectrical current sensors may be eliminated from consideration whencharacterizing operation of refrigeration units, when identifying energyinefficiencies and for the purposes of predicting failure. It has beenobserved that most events, discontinuities and distortions in observedtemperature characteristics can be attributed to human activity. Humanactivity that affects the temperature characteristic includes opening ofthe freezer or refrigerator, moving of the freezer or refrigerator,vibrations caused by human activity affecting airflow, variations inpower supply due to use of electrical equipment and so on. Accordingly,analysis of data may be restricted to certain times of day when humansare not present. For example, freezers in commercial establishments maybe monitored during early morning hours.

In certain embodiments, elements of the cooling system may beinstrumented. For example, some commercially available freezers areshipped with temperature sensors installed in or around compressors,evaporators and conduits that carry Freon or other refrigerants.Typically, the temperature sensors are wired through a harness to one ormore connectors for diagnostic and maintenance. In some embodiments,signals from these additional temperature sensors can be monitored bycontrollers provided according to certain aspects of the invention. Theadditional data derived from these signals can be analyzed to obtainadditional baseline characteristics and to permit more preciseidentification of inefficiencies or dysfunction and to predict systemfailures. For example, temperature readings associated with variousaspects of the cooling system can be used to characterize nominal andoptimal cooling cycles.

In certain embodiments, a vibration sensor, such as a low frequencyaccelerometer, can detect vibration of a compressor. Compressorvibration normally occurs as a compressor is cycling on and/or off. Thesensor can respond to changes in vibration patterns and/or can comparemeasured vibration to previously recorded or observed operating norms.Response may include an increased sampling rate which typically isinterpreted by an application server as an alert. An alert may beprocessed when vibration occurs at an abnormal time interval. Forexample, a longer than normal vibration, or vibration at an unexpectedtime within the compressor cycle. Extended periods of observed vibrationcan provide a baseline for normal compressor function. In at least someembodiments, the vibration sensor may detect unusual vibrations that maybe attributable to external events such as earthquakes or relocation ofa refrigeration unit.

In certain embodiments, performance of refrigeration units, compressorsand alerts can be presented to users at a console or monitor. Users mayaccess a monitoring system using an Internet application or browser,through a smartphone application, using a computer or tablet, or by anysuitable system that supports user interactions with a networked consoleor control application. By means of a graphical user interface accessedvia any Internet browser, user-specific data can be accessed from anapplication server 28 using a distinct login ID/password. Online macrosmay score the performance of each freezer. In one embodiment, a lettergrade of A, B, C or F can be assigned to compare the unit with abaseline grade or peer performance. For example, freezers assigned agrade of C or F can be marked and shut down for preventive maintenance,while freezers rated with an A or a B grade may be allowed to remain inoperation with no planned preventative maintenance.

If preventive maintenance is performed, a user, such as the servicetechnician, can manually enter data into the online system. The user cantrack potential problems, allowing for intervention before failureoccurs. Certain embodiments of the invention may also allow the user toselect a logic function to predict future performance. This function canallow the user to input scenarios that project total cost of ownershipfor each system. The function can also calculate potential savings ofeconomy, if, for example, F-grade freezers were to run at B-gradeefficiency levels. By identifying systems with a low grade, totalnon-operational time for all systems, cost for repair, and risk to thematerials being stored can be reduced.

Certain embodiments of the invention can map 2D images, such as a floorplan, of a structure to create a 3D representation of a refrigerationunit and other objects housed with varying elevation or user-definedregion of the structure. A user can manipulate the 2D image(s), whichcan be reflected in pseudo-real time in the 3D structure, without theuse of a separate animation software. The refrigeration unit can beclassified within a database to control for use and context, and ahierarchical relationship may be created to simplify management of therefrigeration unit and structures. The refrigeration unit may bedisplayed in a single view, including all units on all floors in astructure or multiple structures, or in discrete views by regions.Floors can also be hidden from view in order to simplify multi-floorstructures. Certain embodiments can provide further details of aselected 3D view of a floor to be shown in a 2D map, in order to manageinformation and placement of objects. Other embodiments of the inventioncan enable the user to manipulate the 3D view, while the objectsmaintain position relative to an established coordinate system.

FIG. 7 illustrates an example of a system 700 configured to providecentralized or distributed control of assets and/or monitoring ofassets. Motes 704, 706 may be deployed to communicate with, and/orcontrol sensors that monitor certain aspects of a plurality ofcorresponding refrigeration systems. In a large installation or on alarge campus, a sensor network 702 may be configured to more efficientlycollect and distribute sensor data sampled by Motes 704, 706 fromsensors, and/or from other sources associated with refrigeration assetson the campus. The sensor network 702 may conform to a hierarchicalarchitecture. In one example, a sensor network 702 may have one or morelocal system managers 708 that are deployed to collect and/or aggregatesensor data and other information provided by the Motes 704, 706. Alocal system manager 708 may manage and/or comprise a network ofcontrollers and/or device managers. The Motes 704, 706 and the localsystem manager 708 may communicate through a local network 710, whichmay comprise a wired or wireless network.

The sensor network 702 may be coupled to a processing system 720 througha network 712 that may comprise a proprietary wide area network and/or apublic wide area network such as the Internet. The processing system 720may be centralized or distributed over a plurality of networkedcomputing systems. The processing system 720 may provide a plurality offunctional elements and devices, including a data repository 722, whichmay include a database system, an analysis system 724 that may beconfigured to process and analyze measurements, statistical data andtrends, metadata and other information received from the sensor network702. The analysis system 724 may employ historical data, profiles,design goals and other information maintained by the data repository 722to review, process and otherwise analyze information received from thesensor network 702. The processing system 720 may include a sensormanagement system that monitors sensor operation and can detect failureof one or more sensors using information received or retrieved from thesensor network 702, the analysis system 724 and/or the data repository722.

In certain embodiments, Motes 704, 706 and local system managers 708 ofthe sensor network 702 may communicate using connectionlesscommunications systems. For example, one or more sensors may use amessaging service such as a Short Message System (SMS) cellular or aMultimedia Messaging Service (MMS). Other communications methods may beemployed, including routable networks. In one example, communicationwithin the sensor network 702 and between the sensor network 702 andpublic or private wide area networks may be based on protocols thatestablish a session used to exchange commands and data. In one example,communications may be facilitated through the use of protocols thatestablish a contiguous packet-based data connection utilizing a singleroutable protocol or other session comprised of non-contiguous dataconnections used to exchange commands and data.

FIG. 8 illustrates an example 800 in which a refrigeration system 802 isa monitored asset. For the purposes of this description, a refrigerationsystem 802 may comprise one or more of a freezer, refrigerator, storagespace and/or room in which the temperature is controlled in order tomaintain contents of a storage space or chamber 804 within a desired orrequired temperature range. The concepts disclosed herein apply equallyto systems used to monitor HVAC performance within a building or withinan individual room in a building. The concepts disclosed herein applyequally to systems used to maintain environmental conditions for amaterial that is being transported within a vehicle, container orpackage.

In the illustrated example, the refrigeration system 802 may be fittedwith sensors coupled by a sensor bus 816 with a Mote 818. Each componentmay be instrumented using multiple sensors, two or more of which may beconfigured to measure and/or monitor an environmental condition,operating condition, thermodynamic refrigeration cycle, power supply,and/or other systems, characteristics and attributes associated with therefrigeration system 802. In one example, multiple sensors 832, 834, 836may be provided in the cooled chamber 804 of a monitored refrigerationsystem 802. The combination of a Mote 818 and one or more sensors 832,834, 836 monitored or controlled by the Mote 818 may also be referred toas a smart sensor. In some instances, the sensors 832, 834, 836 mayinclude smart sensors that include internal circuits and modules thatenable the sensors to communicate with a network 830 directly or throughan intermediary device (e.g., Mote 818 or aggregator 206).

Certain duplicate sensors are employed for monitoring equipment locationand/or to measure electrical current, voltage, door position, vibration,temperature, pressure, mechanical strain, vibrations within orsurrounding the refrigeration system 802 and other characteristics ofthe refrigeration system 802. The refrigeration system 802 may have morethan one chamber 804 or other storage space that is maintained at adesired temperature by a refrigeration system 806. In one example, therefrigeration system 806 may include a compressor 810, a heat exchangesystem 812, a refrigerant circulation 814 and other electromechanicalcomponents 808.

In some implementations, a second Mote 826 (or smart sensor) may beprovided to monitor power consumption of one or more refrigerationsystems 802 or other assets using sensors 824 or other instruments. Oneor more sensors 824 may be connected to one or more Motes 826, in asub-panel for example, to collect data from more than one refrigerationsystem. The second Mote 826 may monitor current and/or voltage sensorsassociated with powered components of the refrigeration system 802including, for example, the compressor 810, pumps, valves and otherrotating machinery or electromechanical components 808.

In some instances, the Motes 818, 826 may be implemented using a smartmodule that has a processing circuit, storage and one or moretransceivers operable to connect the Motes 818, 826 to each other or tothe network 830 continuously, continually, opportunistically or when anetwork connection is available. One or more of the Motes 818, 826 mayoperate as a data logger that stores various information received orgenerated by the Motes 818, 826. In some instances, the Motes 818, 826may be adapted to perform certain data processing tasks on sensor data.

In some examples, equipment location, analytical measurements and otherinformation obtained from other sources may include descriptive,qualitative or quantitative data entered manually at or near therefrigeration system 802, or entered through a networked console,terminal, mobile computing device and communicated through the network830. In one example, information representing equipment location and/orother manual measurements taken during the service or maintenance of thesystem may be provided through the mobile computing device 208illustrated in FIG. 2. Power-related measurements may be directly orindirectly obtained from sensors 824, 832, 834, 836, and energyconsumption may be accurately calculated by measuring amperage andvoltage periodically or continuously. In some instances, energyconsumption may be estimated or calculated from secondary measurements,such as temperature variations, thermodynamic refrigeration cyclefrequency, total current consumption by a plurality of assets, etc. Forexample, energy consumption may be determined by correlating temperatureor measurements related to a thermodynamic refrigeration cycle withcurrent flow in an electrical main.

In some instances, a Mote 818, 826 may survey a plurality of sensors824, 832, 834, 836 or otherwise receive measurements from the sensors824, 832, 834, 836 on a periodic basis. The Motes 818, 826 may beconfigured to process the measurements to generate raw statistical dataand perform certain filtering or statistical analyses, such asidentifying trends, transitions from normal to abnormal operation basedon crossings of threshold values, etc. The Motes 818, 826 may beconfigured to store and maintain a history of measurements, trendinformation and other metadata. The Motes 818, 826 may be configured touse a wired or wireless network interface 820, 828 to transmit storedinformation, raw measurements, alarms and status information and maysignal occurrences of exceptions to normal operation to other Motes 820,826 or to a gateway, centralized or distributed data collection ormonitoring system and/or a centralized or distributed controllingsystem. The network interfaces 820, 828 may support communications witha network 830 that may comprise local networks, ad hoc networks,proprietary wide area networks and/or public networks including theInternet.

According to certain aspects, the operational reliability of certainsensors 824, 832, 834, 836 may be determined based on comparisonsbetween sensors measuring the same quantity, or with reference to sensormeasurements of one or more correlated quantity correlated.

Comparative analyses of sensors may be used to dynamically and/orcontinuously calibrate sensors measuring the same quantity. Dynamiccalibration permits a monitoring system to recalibrate the sensors atany time without labor or the need for dedicated calibration equipmentat the system that is instrumented by the sensors.

FIG. 9 illustrates an example of a monitored system 900 that includes arefrigeration device 902 instrumented with sensors that may bedynamically and/or continuously calibrated in accordance with certainaspects disclosed herein. The refrigeration device 902, which maycorrespond in some respects to the freezer 300 of FIG. 3, has multipleshelves 906 a, 906 b, 906 c, 906 d that effectively create multiplezones within the refrigeration device 902, and is instrumented withsensors 910, 912 a, 912 b, 912 c, 914, 916 a, 916 b, 916 c, 916 d thatcommunicate through a mote 918.

Location of the sensors can be selected to accommodate structure of thespace or spaces to be monitored, presence or absence of shelves andother features that may obstruct airflow, including expected location ofstored materials and other factors. In one example, at least one sensor910, 912 a-912 c, 914, 916 a-916 d is provided to measure temperaturewithin each zone defined by sidewalls and shelves 906 a, 906 b, 906 c,906 d of the refrigeration device 902. In other examples, one or morezones may be defined independently of sidewalls and/or shelves 906 a,906 b, 906 c, 906 d of the refrigeration device 902, and each of thesezones may be instrumented using one or more sensors. Sensors 910, 912a-912 c, 914, 916 a-916 d can be placed at various points inthree-dimensional space within a chamber. For example, certain sensors916 a-916 d can be placed in the front, back or middle of a shelf 906 a,906 b, 906 c, 906 d, each shelf may have multiple sensors, other sensors912 a 912 b, 912 c may be placed at any available location on thesurface on or within an inner vertical surface (e.g. sidewall, backwallor door) of the refrigeration device 902, and some sensors 910, 914 beplaced at any available location on an inner horizontal surface of therefrigeration device 902.

In some implementations, sensors may be used to measure vibration,current and/or flow of refrigerants. Location of sensors 910, 912 a-912c, 914, 916 a-916 d may be selected to optimize coverage inthree-dimensional (3D) space. For example, a first sensor 912 a may belocated on a first sidewall of the refrigeration device 902 and a secondsensor 912 c may be located on a second sidewall of the refrigerationdevice 902, where the first sensor 912 a is positioned deeper into thechamber of the refrigeration device 902 than the second sensor 912 c.

The shelves 906 a, 906 b, 906 c, 906 d may be constructed with a varietyof designs. In one example, a shelf 926 a may have a slatted design andmay be supported by hangars 932, whereby objects and materials areplaced on slats 928 that are separated by spaces 930 configured topermit relatively free flow of air within the chamber of therefrigeration device 902. In another example, a shelf 926 b may have asolid planar surface 934 design and may be supported by hangars 932,where the shelf 926 b has a side dimension 936 that is less than thedepth of the chamber of the refrigeration device 902, thereby providinga gap 938 that permits a vertical flow of air within the chamber of therefrigeration device 902.

The shelves 906 a, 906 b, 906 c, 906 d and materials stored in thechamber of the refrigeration device 902 can impede air flow betweenzones of the chamber of the refrigeration device 902 and the reduced orimpeded airflow can cause disparities in readings of the sensors 910,912 a-912 c, 914, 916 a-916 d. Airflow can be different for differentloading conditions of the refrigeration device 902.

FIG. 10 illustrates certain examples of sensor layouts 1000, 1010, 1020,1030, 1040 on a shelf, floor, ceiling or wall of atemperature-controlled asset. The examples relate to the arrangement orconfiguration of five locations 1004 in which sensing devices may belocated, embedded or attached. A single sensor or a cluster of sensors1006 may be deployed at each location 1004. In some implementations,individual sensors in a cluster of sensors 1006 may be monitoredindependently. In some implementations, two or more sensors of the sametype in a cluster of sensors 1006 may be controlled locally and mayreport a combined or average measurement. In some implementations, twoor more sensors of the same type in a cluster of sensors 1006 may becalibrated with respect to one another using certain techniquesdisclosed herein. In some implementations, two or more different typesof sensors may be provided in a cluster of sensors 1006. The number andtype of sensors included in a cluster of sensors 1006 may be determinedon an application-by application basis.

In FIG. 10 the illustrated sensor layouts 1000, 1010, 1020, 1030, 1040represent a subset of the possible layouts that may be defined for anasset or application when locations 1004 are desired or required. Feweror more locations may be defined for one or more of the shelves, floors,ceilings or walls of a temperature-controlled asset. The illustratedsensor layouts 1000, 1010, 1020, 1030, 1040 define a generally symmetricarrangement of locations 1004. In various implementations, non-symmetriclayouts may be configured.

In some implementations, one or more sensors may be individually placed,attached, embedded or fixed to a shelf, wall, floor, ceiling orpartition in a temperature-controlled asset. The one or more sensors maybe removably placed on the shelf, wall, floor, ceiling or partition suchthat a sensor can be relocated at a later time. In one example, the oneor more sensors may be placed without fastening or adhesive. In anotherexample, each sensor may be fixed in place using a fastener, an adhesiveor an adhesive device or material such as VELCRO® or aremovable/restickable glue. In another example, certain sensors may beembedded in the shelf, wall, floor, ceiling or partition duringmanufacture or assembly.

In some implementations, one or more sensors may be grouped, protectedand/or fixed in a desired location using lamination. FIG. 11 illustratesa first lamination example 1100 in which sensors 1112, 1114, 1116 areheld in place by respective laminating sheets 1106, 1108, 1110. A firstsensor 1112 may be held in place against a vertical member 1102 (e.g. asidewall, backwall, door, shelf support element etc.) by a firstlaminating sheet 1106. The first laminating sheet 1106 may be coatedwith an adhesive to provide an adhesive surface can attach the firstlaminating sheet 1106 to the vertical member 1102. A second sensor 1114may be fixed by a second laminating sheet 1108 to a top or upper surfaceof a shelf 1104 or other element that has at least a partial horizontalorientation. A third sensor 1116 may be fixed by a second laminatingsheet 1110 to a bottom or lower surface of the shelf 1104 or otherelement that has at least a partial horizontal orientation.

FIG. 12 illustrates a second lamination example 1200 in which a sensor1206 is sandwiched between two laminating sheets 1202, 1204 to form asensing device. The sensing device may be referred to as a temperaturesensing surface, temperature sensing device, temperature sensing patch,temperature mat, temperature dot, sensor mat, sensor dot, or the like.Certain aspects described in relation to lamination apply equally to adevice that is constructed differently, configured or otherwise used asa partition or shelf and in which one or more sensors 1206 have beenembedded. The sensing device may be employed in a variety ofapplications or locations. In one example, laminating sheets 1202, 1204may be fastened together by an adhesive, heat treatment, crimping, orother technique suitable for the material used to form the laminatingsheets 1202, 1204. In certain implementations, a temperature mat thatcarries or includes one or more sensors may be placed on a removablyplaced on a shelf or floor, such that the temperature mat can be latermoved or removed as needed or desired. In some instances, a temperaturemat can be installed vertically on a shelf, for example, disposedbetween boxes, containers, bottles, drawers, or the like.

In some implementations, an adhesive 1208 applied to the outer surfaceof one laminating sheet 1202 may enable the resultant laminated sensor1210 to be attached to a surface of a device to be monitored. In oneexample, a lower surface of each of the two thermally conductivelaminating sheets 1202, 1204 has an adhesive 1208 applied, and thelaminating sheets 1202, 1204 may be fastened when the adhesive layer onthe lower surface of an upper laminating sheet 1202 is contacted to atleast a portion of the upper surface of a lower laminating sheet 1204,enclosing the sensor 1206 between the laminating sheets 1202, 1204.Laminated sheets 1202, 1204 may comprised of two layers of laminationwith sensors in the middle, placed under, between or on top of storageboxes or against the wall with options for spacing from the wall toavoid thermal transfer.

In a first example, a laminated sensor 1220 includes a single sensor1222, and the laminated sensor 1220 can be mounted and held in placeagainst a surface within a volume to be monitored. The laminated sensor1220 may occupy a relatively small area of the surface. In a secondexample, a laminated sensor 1230 includes multiple sensors 1232, 1234,1236 that may be arranged in a desired configuration before placementwithin the volume to be monitored. Multiple copies of the laminatedsensor 1230 may be manufactured by equipment that can consistentlyreproduce copies of the laminated sensor 1230 such that spatial andtemporal baselines derived from measurements obtained from someinstances of the laminated sensor 1230 can be used to calibrate otherinstances of the laminated sensor 1230. In some implementations, thelaminated sensors 1210, 1220, 1230 may be produced without an adhesive1208 applied to an external laminating sheet 1204.

Referring again to FIG. 9 a monitoring system configured in accordancewith certain aspects disclosed herein can continuously recalibrate thesensors 910, 912 a-912 c, 914, 916 a-916 d to account for changingcircumstances and/or changes in performance of one or more sensors 910,912 a-912 c, 914, 916 a-916 d. The monitoring system may be configuredto use measurements of a static condition of the monitored system 900obtained by a combination of sensors to calibrate or recalibrate one ormore of the sensors 910, 912 a-912 c, 914, 916 a-916 d. The monitoringsystem may be further configured to generate and/or use baselinecharacteristics that represent individual sensors 910, 912 a-912 c, 914,916 a-916 d and combinations of sensors under a variety of operationalconditions. The monitoring system may be configured to generate and/oruse baseline characteristics that represent individual sensors 910, 912a-912 c, 914, 916 a-916 d and combinations of sensors under a variety ofoperational conditions. In some instances, the baseline characteristicsmay reflect information generated for similar sensors deployed inrefrigeration device 902 of the same type and/or baseline informationprovided by a manufacturer of the refrigeration device 902.

In one example, the sensors 910, 912 a-912 c, 914, 916 a-916 d may beinitially calibrated while the shelves 906 a, 906 b, 906 c, 906 d of therefrigeration device 902 are empty. The initial calibration may includestatic and/or dynamic calibration processes. The static calibrationprocess may be performed when the temperature within the chamber of therefrigeration device 902 is stable, as indicated by unchangingtemperature measurements obtained by the sensors 910, 912 a-912 c, 914,916 a-916 d located within the chamber of the refrigeration device 902.A free flow of air can be expected within an empty chamber of therefrigeration device 902, and the temperature throughout the chamber ofthe refrigeration device 902 can be expected to be constant, andtemperature measurements obtained by the sensors 910, 912 a-912 c, 914,916 a-916 d can be expected to be constant, or to be changing veryslowly. In one example, the initial static calibration may includedetermining current temperature within the chamber of the refrigerationdevice 902 based on a pre-calibrated sensor placed within the chamber ofthe refrigeration device 902. In another example, each of the sensors910, 912 a-912 c, 914, 916 a-916 d may be calibrated during manufacture,assembly or servicing of the refrigeration device 902 and the currenttemperature within the chamber of the refrigeration device 902 may bedetermined as the temperature indicated by a majority of the sensors910, 912 a-912 c, 914, 916 a-916 d, or as an average or median of thetemperatures indicated by the sensors 910, 912 a-912 c, 914, 916 a-916 dor a subset of the sensors 910, 912 a-912 c, 914, 916 a-916 d. Staticcalibration processes may be continuously or continually performed afterthe initial calibration.

FIG. 12 illustrates a laminated sensor 1200 that includes an array offive sensors 1202, 1204, 1206, 1208, 1210 and that may bepeer-evaluated. Certain aspects of the peer evaluation techniques areapplicable to arrays of sensors that are mounted independently, and/orconfigured in a variety of 3-dimensional alignments of two or moresensors 912 a-912 c, 914, 916 a-916 d. In the illustrated example,peer-evaluation for single sensors 1202, 1204, 1206, 1208, 1210 isvisualized as a pentagon 1220, and Table 1 below depicts an example inwhich the five sensors 1202, 1204, 1206, 1208, 1210 in an asset arepeer-evaluated. Peer evaluation may employ various machine learning andartificial intelligence techniques, including neural networks, means andpopulation testing, pattern matching, auto-correlation, covariance,analysis of variance (ANOVA), multivariate analysis of variance(MANOVA), autoregressive integrated moving average (ARIMA), seasonalautoregressive integrated moving average (SARIMA) or stepwise regressiontechniques just to name just a few, which may be employed to identifycorrelations among peers in a plurality of sensors, or with respect toother comparable assets or peer groups in a population. For each sensor1202, 1204, 1206, 1208, 1210 under test, a model may be developed thatcan report overall fit, accuracy and confidence interval based uponcorrelation of the sensor 1202, 1204, 1206, 1208, 1210 under test withone or more of its peer sensors 1202, 1204, 1206, 1208, 1210 acting as amulti-variate or linear system. In one example, tests are configured torun periodically to determine if any of the sensors have lostcorrelation or fit.

TABLE 1 Voting Peers Sensor Under (Coefficient of Correlation) Test 1 23 4 5 1 100% 97% 93% 92% 97% 2 100%  96% 95% 99% 3 95% 100%  97% 94% 497% 93% 100%  97% 5 92% 96% 95% 100% 

With reference to FIG. 13, in some implementations, the sensors 1302,1304, 1306, 1308, 1310 in the laminated sensor 1300 or clusters ofindividual sensors 910, 912 a-912 c, 914, 916 a-916 d may be coupled toa sensor hub, where two or more sensors (e.g., thermocouples)communicate through a sensor hub. In one example, initial validationincludes validating the sensor array in a laminated sensor 1300 placedon each of the four shelves 906 a, 906 b, 906 c, 906 d, and/or theindividual sensors 1302, 1304, 1306, 1308, 1310 in each laminated sensor1300. Initial validation of the twenty sensors and the four arrays caninvolve a prolonged process. In some instances, a validation lasting twoor more days may be needed to obtain an initial and highly accuratecharacterization of temperature gradients. Peer evaluation implementedusing voting analytics can be used to characterize and/or calibrateindividual sensors 1302, 1304, 1306, 1308, 1310, one or more laminatedsensor 1300, and the set of four laminated sensor 1300. In one example,autocorrelation techniques may be used to find correlations, andvariances may be analyzed to detect loss of correlation over time withregard to baseline characteristics. Statistical methods and othertechniques can be employed to determine which sensors provide, or areassociated with highest correlations.

After initial validation and calibration, individual sensors and/orlaminated sensors 1300 with highest correlations can be operated withinthe volume or enclosure to be monitored, typically after it has beendetermined that a subset of such sensors can provide a second reliablemeasurement system in substitution or comparison. In some instances,other sensors may be physically removed, disconnected or the dataignored to simplify installation or to reduce deployment costs, andbased on a determination that the quality of their contribution, or lackthereof, to the overall accuracy of the calibration method.

In some implementations, substitute measurements may be provided by thegroup of peers that evaluated and voted on the accuracy of a sensor thatis physically removed, disconnected, deemed unable to be recalibrated torequired limits of accuracy or confidence, or ignored during initial ordynamic calibration. For example, a first sensor under test may bedeemed to be an inaccurate sensor based on voting by a group of 8 peerdevices. Measurements provided by the inaccurate sensor may thereafterbe ignored and replaced by substitute measurements generated from theplurality of sensors not under test, using machine learning andartificial intelligence techniques, including neural networks, means andpopulation testing, pattern matching, auto-correlation, covariance,ANOVA, MANOVA, ARIMA, SARIMA or stepwise regression techniques. Thegroup of 8 peer devices of known accuracy are capable of providinginferred or estimated substitute measurements for the first sensor forthe same reasons the group of 8 peers are able to determine the amountof error for the first sensor.

In one example, a processing system may determine a loss of calibrationor accuracy of the first sensor based on a determination that the firstsensor has lost correlation or covariance with its peers. The processingsystem may ignore measurements provided by the first sensor when thefirst sensor is determined to have lost correlation or covariance with aplurality of peer sensors and to substitute the measurements provided bythe first sensor with estimated measurements generated for the firstsensor by the plurality of peer sensors, with the accuracy andconfidence necessary to deem the substituted values acceptable.

Certain industry organizations define and promulgate standards,practices and guidance for sensor calibration. In the United States forexample, National Institute of Standards and Technology (NIST) traceablecalibration instruments may be used to test or characterize the accuracyof a temperature measurement device. The calibration process includestaking measurements using the sensor and applying corrections to removeany apparent error until the sensor under test and the NIST calibrationinstrument measure the same value.

In another example, a measurement error or calibration error in a sensormay be detected based on a loss of correlation with one or more peerdevices. An amount of correction applied to the sensor under test may besufficient to restore its correlation with the peer devices. In someinstances, the sensor may experience an unexpected or unexplained lossof calibration after a period of time. The loss of calibration may beunexpected or unexplained when it occurs within a period of time that isshorter than may be anticipated for the type of device. The loss ofcalibration may be unexpected or unexplained when calibration occursrepeatedly and at a rate that is greater than expected for the type ofdevice. A system may deem the physical sensor as unusable or defectiveand may substitute deemed measurements of known accuracy derived fromthe historical analysis of correlations of the physical sensor with itspeer devices. The deemed measurements may be generated based onmeasurements obtained from one or more peer devices.

Turning again to FIG. 9, a dynamic calibration process or temperaturevalidation process may be performed when the temperature within thechamber of the refrigeration device 902 varies over time when thechamber of the refrigeration device 902 is empty. A free flow of air canbe expected within an empty chamber of the refrigeration device 902, andthe temperature throughout the chamber of the refrigeration device 902can be expected to be consistent. Cycles in temperature measurementsobtained by the sensors 910, 912 a-912 c, 914, 916 a-916 d can beanalyzed when calibrating the temperature within the chamber of therefrigeration device 902. For example, the measurements obtained by thesensors 910, 912 a-912 c, 914, 916 a-916 d may exhibit a similar cycleand/or range of temperatures, where changes in temperature at onelocation within the chamber lag corresponding changes in temperature atanother point within the chamber. In one example, the initial staticcalibration may include determining current temperature within thechamber of the refrigeration device 902 based on a pre-calibrated sensorplaced within the chamber of the refrigeration device 902. In anotherexample, each of the sensors 910, 912 a-912 c, 914, 916 a-916 d may becalibrated during manufacture, assembly or servicing of therefrigeration device 902 and the current temperature within the chamberof the refrigeration device 902 may be determined as the lag-adjustedtemperature indicated by a majority of the sensors 910, 912 a-912 c,914, 916 a-916 d, or as an average or median of the lag-adjustedtemperatures indicated by the sensors 910, 912 a-912 c, 914, 916 a-916 dor a subset of the sensors 910, 912 a-912 c, 914, 916 a-916 d.

The initial dynamic calibration process may be performed to define theresponsiveness and/or sensitivity of individual sensors 910, 912 a-912c, 914, 916 a-916 d, and may involve the use of a reference sensorsplaced in the chamber of the refrigeration device 902. Dynamiccalibration processes may be continuously or continually performed afterthe initial calibration. Dynamic calibration may be performed as part ofa temperature validation process.

According to certain aspects disclosed herein, dynamic calibration canbe performed while the refrigeration device 902 is in operation. Dynamiccalibration may be accomplished by comparing a current cycle ofmeasurements with baseline cycles. Referring again to FIG. 6, thetemperature characteristic 600 of a freezer compartment, which may bederived from temperatures monitored within the chamber of therefrigeration device 902 can be expected to be cyclic and may correspondto a compression cycle associated with the cooling system of therefrigeration device 902. The compressor or condenser cycle 610 maycorrespond to the compression cycle in the refrigeration device 902.Each sensor 910, 912 a-912 c, 914 can be expected to providemeasurements that follow the compression cycle with a lag with respectto other sensors 910, 912 a-912 c, 914, 916 a-916 d. Calibration mayinvolve a frequency domain analysis of the measurements received fromeach sensor 910, 912 a-912 c, 914, amplitude of temperature measurementsand/or an analysis of lags or changes in lags between sensors 910, 912a-912 c, 914, 916 a-916 d.

In a calibrated system, relationships between temperature cyclesreported by each sensor can be established. In some instances, sensorsmay correctly report different maximum and/or minimum temperatures,reflecting a temperature imbalance within the chamber. The monitoringsystem may recognize that the sensors 910, 912 a-912 c, 914, 916 a-916 dare calibrated when the temperature cycles reported are consistent withbaseline measurements. A sensor may be determined to be out ofcalibration when its reported temperature cycle vary from baselineand/or when differences between its reported temperature cycle and thetemperature cycles reported by one or more other sensors 910, 912 a-912c, 914, 916 a-916 d vary from baseline differences. In one example, aloss of individual sensor calibration may be identified when thedifference in maximum or minimum temperatures of a cycle reported by twoor more sensors 910, 912 a-912 c, 914, 916 a-916 d changes over time.Loss of calibration may be attributed to changes in sensor sensitivityor changes in conditions within the chamber of the refrigeration device902. In some instances, changes in conditions within the chamber may beidentified when variations are detected in the differences reported bymultiple combinations of sensors 910, 912 a-912 c, 914, 916 a-916 d. Forexample, a change of conditions may be indicated when the difference inmaximum temperature reported by a first pair of sensors 910, 912 achanges concurrently with a change in the difference in minimumtemperature reported by a second pair of sensors 912 c, 914.

Changes in conditions within the chamber of the refrigeration device 902may result from addition, relocation or removal of objects on one ormore shelves 906 a, 906 b, 906 c, 906 d. Evidence of such changes may beprovided by sudden changes in the compression cycle triggered by dooropening events. The compression cycle may increase in frequency or thecompressor may remain active for prolonged periods of time. Door openevents may be recognized, and continuous or continual calibrationprocesses may be suspended until the compression cycle has returned toan identifiable equilibrium.

Continuous or continual calibration may enable a monitoring system toadjust temperature readings from one or more sensors 910, 912 a-912 c,914, 916 a-916 d when changes in temperature cycle cannot be attributedto changes in chamber conditions. The ability to continuously orcontinually calibrate sensors 910, 912 a-912 c, 914, 916 a-916 d canreduce maintenance costs and increase system reliability by increasingaccuracy of predictions of failure of the refrigeration device 902.

In some implementations, the monitoring system may use information fromother types of sensors 920, 924 to calibrate the sensors 910, 912 a-912c, 914, 916 a-916 d that measure temperature within the chamber of therefrigeration device 902. In one example, the monitoring system mayreceive measurements of current flow in a power line 908 provided to therefrigeration device 902, where cycles in the current flow can establishthe compression cycle associated with the refrigeration device 902. Inanother example, the monitoring system may receive measurements ofcurrent flow in a power line 922 provided to a power distribution panel904 that feeds multiple refrigeration devices, where analysis of cyclesin the current flow can establish the compression cycle associated withthe refrigeration device 902.

FIG. 14 illustrates certain aspects of a system 1400 that can beconfigured to calibrate and/or detect failure of sensors 1412, 1414,1416 deployed to monitor an asset 1402. A failure or loss of calibrationof a sensor can result in a false prediction or indication of failure inthe monitored asset 1402, or can result in a failure to predict orindicate imminent failure of the monitored asset 1402. According tocertain aspects disclosed herein, the illustrated system 1400 can beconfigured to improve reliability and confidence in the detectionsystems.

In certain examples, the monitored asset 1402 may be a refrigerationunit such as a freezer, an HVAC system, a facility that includes one ormore HVAC system and/or multiple ULT freezers, or another type ofapparatus or facility. For the purposes of this disclosure, the exampleof a monitored asset 1402 that includes an enclosed space 1422 that iscooled by a refrigeration unit 1406. The refrigeration unit 1406 may beprovided within the enclosed space 1422, in another space within themonitored asset 1402, or external to the monitored asset 1402. In theillustrated example, the enclosed space 1422 is monitored using threesensors 1412, 1414, 1416 that may be nominally equivalent and that maybe located in close proximity or provided at different locations withinthe enclosed space 1422.

In one example, the sensors 1412, 1414, 1416 measure temperature and, inan ideal case, produce the same temperature readings measured within theenclosed space 1422. In a practical implementation, the sensors 1412,1414, 1416 may produce different output values. In some implementations,any two sensors 1412, 1414, 1416 may produce output values that areoffset from one another due when placed in different locations withinthe enclosed space 1422. In some instances, any two sensors 1412, 1414,1416 may produce output values that are offset from one another due tovariations in location, operating characteristics caused by differencesin manufacturing process, voltage and/or temperature (PVT). The offsetsmay be constant or proportional in size. Different sensors 1412, 1414,1416 may exhibit different dynamic performance such that a first sensor1412, 1414, 1416 may react at a different rate to changes in temperaturethan a second sensor 1412, 1414, 1416. Furthermore, differences inoutput values produced by collocated sensors 1412, 1414, 1416 may changeover time.

According to certain aspects disclosed herein, a monitoring system 1404may assess the condition of each sensor 1412, 1414, 1416 by comparingthe output of the sensor 1412, 1414, 1416 with the output of othersensors 1412, 1414, 1416, 1418. In one example, the output of the threecollocated temperature sensors 1412, 1414, 1416 may be compared toidentify any variances in output. A voting system may be implementedthat can be used to eliminate from consideration a measured value thatis significantly different from the values produced by the other sensors1412, 1414, 1416. When, for example, a first sensor 1412, 1414, 1416fails suddenly, it may produce an output value that is significantlydifferent from the values produced by the other sensors 1412, 1414, 1416and comparison logic (e.g., a set of comparators 1408) may detect thedifference and cause the output of the first sensor to be disregarded.In some instances, the output of the first sensor 1412, 1414, 1416 maybe compared to the decision of the voting system at each sample event inorder to determine whether the output of the first sensor 1412, 1414,1416 should continue to be disregarded by the voting system.

Total failure of sensors 1412, 1414, 1416 may be less frequent thangradual degradation of performance. According to certain aspectsdisclosed herein, the health and/or performance of each sensor 1412,1414, 1416 may be monitored by developing one or more profiles of thesensor 1412, 1414, 1416 with respect to other sensors 1412, 1414, 1416,1418. In the illustrated example, the outputs of the three collocatedsensors 1412, 1414, 1416 are compared, conceptually by the set ofcomparators 1408 that compares the outputs of different pairs of sensors1412, 1414, 1416 to determine an offset between pairs of sensors 1412,1414, 1416. Comparison between three or more sensors 1412, 1414, 1416may be performed in support of a voting system and/or to produce anaverage of the output values produced by the collocated sensors 1412,1414, 1416.

Offsets between values produced by pairs of sensors 1412, 1414, 1416 maybe correlated with a baseline characteristic obtained for the monitoredasset 1402. Variations in offset values may be characterized based on acycle of temperature, thermodynamic refrigeration cycle or other cyclefor which a baseline characteristic has been obtained or generated. Ahistory of offset values may be obtained for pairs of sensors 1412,1414, 1416 and trends in the historical offset values may be determined,such that the baseline characteristic may be updated. Decisions 1420produced by the monitoring system 1404 may include a current temperatureof the enclosed space 1422 obtained as an average, and/or by correctingdifferences between pairs of sensors 1412, 1414, 1416 using baselineoffset characteristics. The monitoring system 1404 may also includealerts, warnings and alarms in the decisions 1420 that may relate totemperature levels in the enclosed space 1422, operation of therefrigeration unit 1406 and/or failure or impending failure of one ormore sensors 1412, 1414, 1416.

Baseline characteristics may also be generated to characterizecorrelations between different types of sensors. In one example,correlation logic 1410 may be employed to correlate temperature valuesproduced by collocated sensors 1412, 1414, 1416 and current, vibration,pressure, temperature or other measurements obtained from refrigerationunit 1406. The correlation logic 1410 may identify failures or otherdeviations of an individual sensor 1418 based on a baselinecharacteristic that correlates measurements obtained from one or moreother sensors 1412, 1414, 1416, 1418.

In accordance with certain aspects of the disclosure, continuous orcontinual calibration processes may be employed in predictive failuremonitoring systems and may be included in continuous validation systemthat satisfy requirements for accreditation by government and/orindustry standards bodies. Accreditation for cold-storage installationsand biorepository applications, refrigerators, ULT systems and liquidnitrogen storage tanks have been developed to ensure that high-valuetemperature-sensitive biological materials are properly stored. It isnecessary to maintain continuously stable temperatures at multiplepoints inside the storage chamber.

Storage conditions may be regulated by internal or external policies,and temperature mapping and validation testing is typically required tobe conducted at periodic intervals, such as at every six-months,one-year, two-years etc., on each cold-storage system. Temperaturemapping and validation testing can assess and document the uniformityand stability of temperatures in comparison to published accreditationstandards. Conventional validation testing can be expensive because itis labor intensive and requires the use of expensive testinstrumentation and/or outsourced calibration services. The failure tohave or maintain a validated system can result in loss of accreditationstatus, and can also result in the loss of high-value inventory orresearch material if temperature anomalies are not controlled. The useof the continuous or continual calibration processes disclosed hereincan reduce the cost of achieving and maintaining compliance by 90% ormore.

Certain aspects of the disclosure enable the automatic detection andcorrection of calibration errors. Continuous validation may beimplemented using a model that can determine and confirm the accuracy ofa measurement from one sensor under test based, a priori, upon itsaccuracy in correlation to each individual and the plurality of sensorscomprising its peers. In certain implementations, covariance and “fit”or correlation can be used to measure and/or assess the relationship andthe inter-dependencies between two or more variables, and the accuracyof the model increases in proportion to increases in the number ofpeers. The ability to determine the accuracy and confidence of eachsensor 910, 912 a-912 c, 914 enables detection of sensor failure basedon changes over time, including changes indicative of a loss ofcorrelation. An alert detection may be produced when predefined orpreconfigured thresholds are exceeded, indicating sensor failure. Insome instances, a sensor 910, 912 a-912 c, 914 can be recalibratedprogrammatically without removal of the sensor and/or other manual orin-person intervention. Maintenance involving continuous and/orcontinual calibration procedures in accordance with certain aspects ofthis disclosure can be effectively performed more frequently thanconventional processes and, in one example, may be performed on a dailybasis.

FIG. 15 illustrates an example 1500 of an architecture for a Mote 1502.With continued reference to FIGS. 1-10, the Mote 1502 may be configuredto connect to a network 830 by any suitable access technology. In oneexample, the Mote 1502 includes a processing circuit 1504 that maycomprise one or more of a microprocessor, a microcontroller, a digitalsignal processor (DSP), sequencing logic, a state machine, and/or othersuch devices. The Mote 1502 and/or processing circuit may also include avariety of commonly used devices and components such as non-transitorystorage, light emitting diode (LED) lamps or indicators, buttons orswitches and/or an audible alarm indicator. The Mote 1502 may include acommunications transceiver 1518 that includes radio frequency, opticalor infrared transmitters and/or receivers. The Mote 1502 may communicatewith one or more sensors 1522, 1524, including sensors 1522 that areincorporated in or integrated with the Mote 1502 and/or external sensors1524 that may be coupled to the Mote 1502 using wired physicalconnectors and/or wireless communications. The Mote 1502 mayadditionally include a global positioning system receiver (not shown), adisplay controller 1530, and user input controllers or drivers 1528 thatmay interface with devices such as a keypad, touchscreen or the like.

The processing circuit 1504 may include one or more analog-to-digital(A/D) converters 1526 configured to receive analog inputs from one ormore of the sensors 1522 and/or 1524 for example, and one or moredigital-to-analog (D/A) converters 1532. The processing circuit 1504 mayinclude one or more sensors 1522 and/or sensor control circuits. Forexample, certain sensors may be provided in an integrated circuitdevice, on a chip carrier or circuit board that carries the processingcircuit 1504. The processing circuit 1504 may be configurable to connectto one or more external sensors 1524. The sensors 1522, 1524 may includetransducers that can be used to sense or measure door position,pressure, acceleration, temperature, humidity, magnetic field, light,load, inclination, radio frequency identification (RFID) signals and orRFID return signals, whether related to a passive or active RFID tag.The processing circuit 1504 may include a battery or energy scavengingdevice and a wired, wireless, infrared, or magnetically coupledinterface that is coupled to an antenna 1520 used for communications.

FIG. 16 is a data flow diagram 1600 that illustrates certain aspects ofsensor data processing and analysis using sensor streams 1602. A sensorstream 1602 may comprise a time series of sensor data streamed from asensor or replayed from storage. The sensor streams 1602 may be derivedfrom sensor data provided by one or more Motes and may be processedusing one or more algorithms 1604. The sensor data algorithms mayinclude simple measures such as maximum and minimum alarm points, ormore robust methods such as a standard deviation algorithm used toobserve uniformity within windows of a sensor stream 1602. Thealgorithms may include a moving variance algorithm used to detect statechanges in various elements of a system, including state changes in acompressor, for example. The algorithms may include a temporal patternmatcher algorithm configured to compare windows of sensor data forsimilarity scoring using one or more distance functions. The algorithmsmay include a terminating Euclidean distance measure algorithm or othersimple distance function for pattern matching. The algorithms mayinclude a Jenks natural breaks algorithm, a K-Means clustering algorithmor another clustering algorithm that provides a mechanism for clusteringsensor data to separate states and/or to find aggregate values.

Certain features 1606 may be extracted from processing the sensor data.The features 1606 may include cycle counts indicating the number ofcompressor cycles in a configured period, based on amperage/energysensor or other data. The features 1606 may include energy usageexpressed as kilowatt-hours (kWh) or the like, and determined as thearea under a power curve for a window of sensor data. Other values maybe derived from energy usage including ampere-hours, for example. Amperehours may be calculated using the formula Ampere Hours=kWh/(1000*volts).The features 1606 may include cycle variance, which may correspond tothe variance in duration of cycles such as thermodynamic refrigerationcycles. Cycle variance may be used to indicate instability in acomponent or element of an asset when other factors remain stable. Thefeatures 1606 may include detected failure patterns, which maycorrespond to a Pattern ID that can be matched in a pattern matchingprocess to identify a known issue associated with a pattern exhibited inthe sensor data. The features 1606 may include detected healthypatterns, which may correspond to a Pattern ID that can be matched in apattern matching process to identify expected behavior associated with apattern exhibited in the sensor data.

Certain analytics methods 1608 may be applied to discover meaningfulpatterns and behaviors from the sensor data. A statistical analysis maybe used to examine features as a sample from one or more like sensors tofind population norms. Benchmarking may be employed to compare thefeatures and/or characteristics of multiple sensors to determine thedistribution of values within a population, and to use the correspondingpercentile to score the feature of that asset. A time-series analysismay be applied to identify features for a single sensor over time,and/or to determine trends or changes, which may indicate the onset offailure. Asset classification may be used to classify or tag assetsbased on computed values, changes over time, etc. Asset classificationmay consider all data to determine if an asset should be tagged forrepair, for example. Asset tags can be added or removed based on trends.

Other data may be considered in addition to the sensor data. In oneexample, a fusion of sensor data and disparate data elements may beemployed to learn new things and to collect or categorize information indifferent ways and for different sources. For example, one or moresensors may be employed to monitor plug load energy consumption in adefined area such as in a room. Plug load energy is consequently anobject that can be benchmarked in a manner similar to other objects suchas compressors used in a refrigeration system. Such sensor data may befused with other data to detect human activity and energy intensity inthe area. In one example, the placement of additional equipment in theroom may indicate a new use pattern for the area, or more or less wateror lighting being used than before or in comparison to other objects.These new data from external sources may be mined to derive a betterunderstanding of energy utilization relative to other monitored objectsdeemed to be similar or comparable.

One or more functions 1610 may be informed by the results produced bythe analytics methods 1608. Dynamic calibration may be performedcontinuously and failure prediction states may be configured for eachmonitored asset. The failure prediction states may be communicated usinga grading system that can be expressed graphically and/or textually. Forexample, a color-coding scheme may be applied to a graphical displayindicating current performance metrics, such as power consumption, cyclevariance, temperature curves, and the like. An asset may be graded usinga configurable and/or familiar color-coding system (Green, Yellow andRed), such that the performance of each asset can be determined incomparison to known achievable levels of performance and energyefficiency to its peers (same make/model) in the population. Gradingscores may be derived from sensor data obtained from sensors associatedwith assets deployed in a variety of settings, locations, andconfigurations.

In one example, assets performing within expectations may be color-codedas Green assets, while underperforming may be coded as Yellow or Redassets. A higher grade (Green) may indicate that the asset is deemed tobe operating efficiently, and/or may be consuming less energy than theaverage of its peers. A next grade (Yellow) may indicate that the scoredasset is exhibiting signs of stress and is consuming more energy thanthe average of its peers.

In one example, a proxy profile may be used to temporarily assign amake/model profile to an asset. An administrator may select a proxy,which represents a similar asset in terms of size, age and construction.Once it has been determined that the database contains a sufficientlylarge sample for the target make/model, then the proxy or the make/modelgroup may be removed and the system may score the assets using theautomated scoring methodology described above using empirical data.

A benchmark scoring process may be run automatically once per month andprior history is retained by the system such that asset grade changes,attributable to changes in asset performance, can be tracked over timefor each individual asset.

FIG. 17 is a conceptual diagram 1700 illustrating a simplified exampleof a hardware implementation for an apparatus employing a processingcircuit 1702 that may be configured to perform one or more functionsdisclosed herein. In accordance with various aspects of the disclosure,an element, or any portion of an element, or any combination of elementsas disclosed herein. The processing circuit 1702 may include one or moreprocessors 1704 that are controlled by some combination of hardware andsoftware modules. Examples of processors 1704 include microprocessors,microcontrollers, digital signal processors (DSPs), field programmablegate arrays (FPGAs), programmable logic devices (PLDs), state machines,sequencers, gated logic, discrete hardware circuits, and other suitablehardware configured to perform the various functionality describedthroughout this disclosure. The one or more processors 1704 may includespecialized processors that perform specific functions, and that may beconfigured, augmented or controlled by one of the software modules 1716.For example, the processing circuit may be configured as communicationsprocessors and other processors that are adapted to handle encoding anddecoding of data for transmission on one or more wired or wirelessnetworks. The one or more processors 1704 may be configured through acombination of software modules 1716 loaded during initialization, andmay be further configured by loading or unloading one or more softwaremodules 1716 during operation.

In the illustrated example, the processing circuit 1702 may beimplemented with a bus architecture, represented generally by the bus1710. The bus 1710 may include any number of interconnecting buses andbridges depending on the specific application of the processing circuit1702 and the overall design constraints. The bus 1710 links togethervarious circuits including the one or more processors 1704, and storage1706. Storage 1706 may include memory devices and mass storage devices,and may be referred to herein as computer-readable media. The bus 1710may also link various other circuits such as timing sources, timers,peripherals, voltage regulators, and power management circuits. A businterface 1708 may provide an interface between the bus 1710 and one ormore line interface circuits or transceivers 1712. Transceivers 1712 mayinclude encoders, decoders, and radio frequency transmitters andreceivers that are used in communicating with various other apparatusover a wired or wireless network or through an opportunistic wirelessconnection. Depending upon the nature of the apparatus, a user interface1718 (e.g., keypad, display, speaker, microphone, joystick) may also beprovided, and may be communicatively coupled to the bus 1710 directly orthrough a bus interface 1708.

A processor 1704 may be responsible for managing the bus 1710 and forgeneral processing that may include the execution of software stored ina computer-readable medium that may include the storage 1706. In thisrespect, the processing circuit 1702, including the processor 1704, maybe used to implement any of the methods, functions and techniquesdisclosed herein. The storage 1706 may be used for storing data that ismanipulated by the processor 1704 when executing software, and thesoftware may be configured to implement any one of the methods disclosedherein.

One or more processors 1704 in the processing circuit 1702 may executesoftware. Software shall be construed broadly to mean instructions,instruction sets, code, code segments, program code, programs,subprograms, software modules, applications, software applications,software packages, routines, subroutines, objects, executables, threadsof execution, procedures, functions, algorithms, etc., whether referredto as software, firmware, middleware, microcode, hardware descriptionlanguage, or otherwise. The software may reside in computer-readableform in the storage 1706 or in an external computer readable medium. Thecomputer-readable medium and/or storage 1706 may be a non-transitorycomputer-readable medium. A non-transitory computer-readable mediumincludes, by way of example, a magnetic storage device (e.g., hard disk,floppy disk, magnetic strip), an optical disk (e.g., a compact disc (CD)or a digital versatile disc (DVD)), a smart card, a flash memory device(e.g., a “flash drive,” a card, a stick, or a key drive), a randomaccess memory (RAM), a read only memory (ROM), a programmable ROM(PROM), an erasable PROM (EPROM), an electrically erasable PROM(EEPROM), a register, a removable disk, and any other suitable mediumfor storing software and/or instructions that may be accessed and readby a computer. The computer-readable medium and/or storage 1706 may alsoinclude, by way of example, a carrier wave, a transmission line, and anyother suitable medium for transmitting software and/or instructions thatmay be accessed and read by a computer. The computer-readable mediumand/or the storage 1706 may reside in the processing circuit 1702, inthe processor 1704, external to the processing circuit 1702, or bedistributed across multiple entities including the processing circuit1702. The computer-readable medium and/or storage 1706 may be embodiedin a computer program product. By way of example, a computer programproduct may include a computer-readable medium in packaging materials.Those skilled in the art will recognize how best to implement thedescribed functionality presented throughout this disclosure dependingon the particular application and the overall design constraints imposedon the overall system.

The storage 1706 may maintain software maintained and/or organized inloadable code segments, modules, applications, programs, etc., which maybe referred to herein as software modules 1716. Each of the softwaremodules 1716 may include instructions and data that, when installed orloaded on the processing circuit 1702 and executed by the one or moreprocessors 1704, contribute to a run-time image 1714 that controls theoperation of the one or more processors 1704. When executed, certaininstructions may cause the processing circuit 1702 to perform functionsin accordance with certain methods, algorithms and processes describedherein.

Some of the software modules 1716 may be loaded during initialization ofthe processing circuit 1702, and these software modules 1716 mayconfigure the processing circuit 1702 to enable performance of thevarious functions disclosed herein. For example, some software modules1716 may configure internal devices and/or logic circuits 1722 of theprocessor 1704, and may manage access to external devices such as theinterfaces or transceivers 1712, the bus interface 1708, the userinterface 1718, timers, mathematical coprocessors, and so on. Thesoftware modules 1716 may include a control program and/or an operatingsystem that interacts with interrupt handlers and device drivers, andthat controls access to various resources provided by the processingcircuit 1702. The resources may include memory, processing time, accessto the transceivers 1712, the user interface 1718, and so on.

One or more processors 1704 of the processing circuit 1702 may bemultifunctional, whereby some of the software modules 1716 are loadedand configured to perform different functions or different instances ofthe same function. The one or more processors 1704 may additionally beadapted to manage background tasks initiated in response to inputs fromthe user interface 1718, the transceivers 1712, and device drivers, forexample. To support the performance of multiple functions, the one ormore processors 1704 may be configured to provide a multitaskingenvironment, whereby each of a plurality of functions is implemented asa set of tasks serviced by the one or more processors 1704 as needed ordesired. In one example, the multitasking environment may be implementedusing a timesharing program 1720 that passes control of a processor 1704between different tasks, whereby each task returns control of the one ormore processors 1704 to the timesharing program 1720 upon completion ofany outstanding operations and/or in response to an input such as aninterrupt. When a task has control of the one or more processors 1704,the processing circuit is effectively specialized for the purposesaddressed by the function associated with the controlling task. Thetimesharing program 1720 may include an operating system, a main loopthat transfers control on a round-robin basis, a function that allocatescontrol of the one or more processors 1704 in accordance with aprioritization of the functions, and/or an interrupt driven main loopthat responds to external events by providing control of the one or moreprocessors 1704 to a handling function.

FIG. 18 is a flowchart 1800 illustrating an example of a calibrationprocess that may be performed in accordance with certain aspectsdisclosed herein. The calibration ay be performed by a monitoringsystem. The calibration may be based on or include a peer evaluationthat may employ various machine learning and artificial intelligencetechniques, including neural networks, means and population testing,pattern matching, autocorrelation, covariance, ANOVA, MANOVA, ARIMA,SARIMA or stepwise regression techniques or the like. These techniquesmay be employed to identify correlations among peers in a plurality ofsensors, or with respect to other comparable assets or peer groups in apopulation. For each sensor under test, a model may be developed thatcan report overall fit, accuracy and confidence interval based uponcorrelation of the sensor under test with one or more of its peersensors acting as a multi-variate or linear system. In one example,tests are configured to run periodically to determine if any of thesensors have lost correlation or fit.

At block 1802, the monitoring system may receive measurements capturedby a plurality of sensors that includes a first sensor deployed within atemperature-controlled asset. In some examples, the measurements may berelated to temperatures within a temperature-controlled chamber of oneor more refrigeration assets. At block 1804, the monitoring system mayidentify a difference between a first temperature obtained frommeasurements provided by the first sensor and a second temperatureobtained from measurements provided by at least one sensor. At block1806, the monitoring system may calibrate the first sensor based on thedifference between the first temperature measurement and the secondtemperature measurement. In one example, each sensor in the plurality ofsensors is deployed within the temperature-controlled asset. In someexamples, one or more sensors may be deployed in a differenttemperature-controlled asset, or may correspond to sensors in a modeledor simulated asset. In some examples, the second temperature measurementrelates to a comparable asset, a peer group of assets, a population ofassets or a simulated asset.

In one example, the second temperature comprises a baseline temperatureobtained from measurements previously received from the first sensor. Inanother example, the second temperature is obtained from measurementsprovided by two or more other sensors. The monitoring system maydetermine that the first sensor is out of calibration when themeasurements provided by the two or more sensors are consistent with oneanother and inconsistent with the measurements provided by the firstsensor.

In one example, the monitoring system may identify the differencebetween the first temperature and the second temperature by performing afrequency domain analysis of the first temperature and the secondtemperature.

In certain examples, the monitoring system may determine that the firstsensor is out of calibration based on measurements of current consumedby the refrigeration asset. The plurality of sensors may be calibratedprior to initial operation, and the monitoring system may calibratedifferences in measurements provided by pairs of sensors in theplurality of sensors after calibration. The monitoring system mayrecalibrate the differences in measurements provided by the pairs ofsensors after a change in conditions within the temperature-controlledchamber. The conditions within the temperature-controlled chamber may bechanged when an object is added to the temperature-controlled chamber.The conditions within the temperature-controlled chamber may be changedwhen an object is removed from the temperature-controlled chamber. Theconditions within the temperature-controlled chamber may be changed whenan object is moved within the temperature-controlled chamber.

In certain examples, the monitoring system may determine a loss ofcalibration or accuracy of the first sensor based on a determinationthat the first sensor has lost correlation or covariance with its peers.The monitoring system may ignore measurements provided by the firstsensor when the first sensor is determined to have lost correlation orcovariance with a plurality of peer sensors and substitute themeasurements provided by the first sensor with estimated measurementsgenerated for the first sensor by the plurality of peer sensors, withthe accuracy and confidence necessary to deem the substituted valuesacceptable, provided by the monitoring system.

In some instances, measurements captured at a plurality of refrigerationassets by one or more smart sensors, Motes, or smart modules may beconfigured to communicate the measurements through a datalogger. Thedata logger may be implemented using circuits or modules of the smartsensors, Motes, or smart modules. The datalogger may store or otherwisemaintain sensor data and other information that can be communicatedthrough a network after the datalogger has identified or established anetwork connection. In certain examples, information collected fromsmart sensors, Motes, or smart modules may be transmitted to an analysissystem through a network at a predefined rate (e.g. every 4 or 8minutes) as a bundle of observations made a faster rate (e.g., every 30second or every minute), and/or at the earlier of a longer-term timer (1hour) or alarm. In some examples, the datalogger, smart sensors, Motes,or smart modules may be adapted to execute one or more data processingalgorithms using the sensor data. In some examples, the datalogger,smart sensors.

FIG. 19 illustrates an example of a hardware implementation for anapparatus 1900 employing a processing circuit 1902. In this example, theprocessing circuit 1902 may be implemented with a bus architecture,represented generally by the bus 1916. The bus 1916 may include anynumber of interconnecting buses and bridges depending on the specificapplication of the processing circuit 1902 and the overall designconstraints. The bus 1916 links together various circuits including oneor more processors, represented generally by the processor 1912, andcomputer-readable media, represented generally by the processor-readablestorage medium 1914. One or more timers may be connected to the busand/or may be directly accessible or embodied in a processor 1912. Thebus 1916 may also link various other circuits such as timing sources,timers, peripherals, voltage regulators, and power management circuits.One or more transceivers 1920 may provide wired communications, orwireless communications through the operation of at least one antenna1918. Depending upon the nature of the apparatus, a user interface maybe provided to support devices such as a keypad, a display, a speaker, amicrophone, a joystick, and the like.

The processor 1912 is responsible for managing the bus 1916 and generalprocessing, including the execution of software stored on theprocessor-readable storage medium 1914. The software, when executed bythe processor 1912, causes the processing circuit 1902 to perform thevarious functions described supra for any particular apparatus. Theprocessor-readable storage medium 1914 may be used for storing data thatis manipulated by the processor 1912 when executing software. Theprocessor-readable storage medium 1914 may also be used for storingsystem information related to one or more remotely managed devices (e.g.profiles), and the apparatus 1900 itself.

In one configuration the processing circuit 1902 may perform one or morefunctions in a system configured to monitor and/or manage a populationof refrigeration systems. The processing circuit 1902 may include amodule or circuit 1904 configured to receive and process measurementscaptured at a plurality of refrigeration assets, a module or circuit1906 configured to determine trends in temperature or temperature cyclesobserved by multiple sensors, and a module or circuit 1908 configured todynamically calibrate sensors.

In one implementation, an apparatus for managing refrigeration systemscomprises one or more communication interfaces, a sensor interfacecircuit and a processing circuit. The communication interfaces mayinclude a wireless communication interface configured to couple theapparatus to a wireless communication network. The sensor interfacecircuit may be configured to receive measurements captured by aplurality of sensors that includes a first sensor deployed within atemperature-controlled asset. The measurements may be related totemperatures within a temperature-controlled chamber of therefrigeration asset. The processing circuit may be configured toidentify a difference between a first temperature obtained frommeasurements provided by the first sensor and a second temperatureobtained from measurements provided by at least one other sensor, andcalibrate the first sensor based on the difference between the firsttemperature measurement and the second temperature measurement.

In one example, the second temperature comprises a baseline temperatureobtained from measurements previously received from the first sensor. Inone example, the second temperature is obtained from measurementsprovided by two or more other sensors. The processing circuit may beconfigured to determine that the first sensor is out of calibration whenthe measurements provided by the two or more other sensors areconsistent with one another and inconsistent with the measurementsprovided by the first sensor.

In some examples, the processing circuit is configured to perform afrequency domain analysis of the first temperature and the secondtemperature and identify the difference between the first temperatureand the second temperature based on the frequency domain analysis.

In certain examples, the processing circuit may be configured todetermine that the first sensor is out of calibration based onmeasurements of current consumed by the refrigeration asset. Theprocessing circuit may be configured to calibrate the plurality ofsensors prior to initial operation and calibrate differences inmeasurements provided by pairs of sensors in the plurality of sensorsafter calibration. The processing circuit may be configured torecalibrate the differences in measurements provided by the pairs ofsensors after a change in conditions within the temperature-controlledchamber. Conditions within the temperature-controlled chamber may bechanged when an object is added, removed or moved.

In certain examples, the processing circuit may be configured todetermine a loss of calibration or accuracy of the first sensor based ona determination that the first sensor has lost correlation or covariancewith its peers. The processing circuit may be configured to ignoremeasurements provided by the first sensor when the first sensor isdetermined to have lost correlation or covariance with a plurality ofpeer sensors and to substitute the measurements provided by the firstsensor with estimated measurements generated for the first sensor by theplurality of peer sensors with the accuracy and confidence necessary todeem the substituted values acceptable.

The processor-readable storage medium 1914 may be configured with codethat causes the processing circuit 1902 to receive measurements capturedby a plurality of sensors that includes a first sensor deployed within atemperature-controlled asset, the measurements being related totemperatures within a temperature-controlled chamber of therefrigeration asset, identify a difference between a first temperatureobtained from measurements provided by the first sensor and a secondtemperature obtained from measurements provided by at least one othersensor, and calibrate the first sensor based on the difference betweenthe first temperature and the second temperature. In one example, eachsensor in the plurality of sensors is deployed within thetemperature-controlled asset. In some examples, one or more sensors maybe deployed in a different temperature-controlled asset, or maycorrespond to sensors in a modeled or simulated asset. In some examples,the second temperature measurement relates to a comparable asset, a peergroup of assets, a population of assets or a simulated asset.

In one example, the second temperature comprises a baseline temperatureobtained from measurements previously received from the first sensor. Inone example, the second temperature is obtained from measurementsprovided by two or more other sensors.

In certain examples, the code may cause the processing circuit 1902 todetermine that the first sensor is out of calibration when themeasurements provided by the two or more other sensors are consistentwith one another and inconsistent with the measurements provided by thefirst sensor. In certain examples, the code may cause the processingcircuit 1902 to identify the difference between the first temperatureand the second temperature by performing a frequency domain analysis ofthe first temperature and the second temperature. The code may cause theprocessing circuit 1902 to determine that the first sensor is out ofcalibration based on measurements of current consumed by therefrigeration asset.

In certain examples, the code may cause the processing circuit 1902 tocalibrate the plurality of sensors prior to initial operation andcalibrate differences in measurements provided by pairs of sensors inthe plurality of sensors after calibration. The code may cause theprocessing circuit 1902 to recalibrate the differences in measurementsprovided by the pairs of sensors after a change in conditions within thetemperature-controlled chamber. Conditions within thetemperature-controlled chamber are changed when an object is added tothe temperature-controlled chamber. Conditions within thetemperature-controlled chamber are changed when an object is removedfrom the temperature-controlled chamber. Conditions within thetemperature-controlled chamber are changed when an object is movedwithin the temperature-controlled chamber.

In certain examples, the code may cause the processing circuit 1902 todetermine a loss of calibration or accuracy of the first sensor based ona determination that the first sensor has lost correlation or covariancewith its peers. The code may cause the processing circuit 1902 to ignoremeasurements provided by the first sensor when the first sensor isdetermined to have lost correlation or covariance with a plurality ofpeer sensors and to substitute the measurements provided by the firstsensor with estimated measurements generated for the first sensor by theplurality of peer sensors with the accuracy and confidence necessary todeem the substituted values acceptable.

In accordance with certain aspects of the disclosure, a temperaturesensing device includes one or more sensors attached to or embedded in afirst layer. The temperature sensing device may be used duringcalibration of a sensor under test. The sensor under test may becalibrated using any of the methods or techniques disclosed herein,including the various methods described in relation to the flowchart1800 of FIG. 18. In one example, the temperature sensing device isformed as a laminated device, where a second layer is attached to thefirst layer. The one or more sensors may be provided between the firstlayer and the second layer. In one example, the temperature sensingdevice may be configured for use as a shelf, wall, floor, ceiling orpartition in a temperature-controlled asset. In another example, thetemperature sensing device may be attached to or cover at least aportion of a shelf, wall, floor, ceiling or partition in atemperature-controlled asset. In the latter example, the temperaturesensing device may be formed as a patch that can be placed at a desiredlocation on the shelf, wall, floor, ceiling or partition in thetemperature-controlled asset.

In one example, the sensor under test is included in the one or moresensors provided in the temperature sensing device. The temperaturesensing device is placed in a temperature-controlled asset after thesensor under test is calibrated. In some instances, each of a pluralityof sensors in the temperature sensing device is calibrated using themethods or techniques disclosed herein and the temperature sensingdevice may be placed in a temperature-controlled asset after at leastone sensor under test is calibrated.

In another example, the temperature sensing device may include one ormore pre-calibrated devices, and the temperature sensing device may beplaced within a temperature-controlled asset to validate or calibratethe sensor under test. In some instances, the temperature sensing devicemay be removed from the temperature-controlled asset after the sensorunder test has been calibrated.

It is to be understood that the specific order or hierarchy of steps inthe methods disclosed is an illustration of exemplary processes. Basedupon design preferences, it is understood that the specific order orhierarchy of steps in the methods may be rearranged. The accompanyingmethod claims present elements of the various steps in a sample order,and are not meant to be limited to the specific order or hierarchypresented unless specifically recited therein.

The previous description is provided to enable any person skilled in theart to practice the various aspects described herein. Variousmodifications to these aspects will be readily apparent to those skilledin the art, and the generic principles defined herein may be applied toother aspects. Thus, the claims are not intended to be limited to theaspects shown herein, but are to be accorded the full scope consistentwith the language of the claims, wherein reference to an element in thesingular is not intended to mean “one and only one” unless specificallyso stated, but rather “one or more.” Unless specifically statedotherwise, the term “some” refers to one or more. A phrase referring to“at least one of” a list of items refers to any combination of thoseitems, including single members. As an example, “at least one of: a, b,or c” is intended to cover: a; b; c; a and b; a and c; b and c; and a, band c. All structural and functional equivalents to the elements of thevarious aspects described throughout this disclosure that are known orlater come to be known to those of ordinary skill in the art areexpressly incorporated herein by reference and are intended to beencompassed by the claims. Moreover, nothing disclosed herein isintended to be dedicated to the public regardless of whether suchdisclosure is explicitly recited in the claims. No claim element is tobe construed under the provisions of 35 U.S.C. § 112, sixth paragraph,unless the element is expressly recited using the phrase “means for” or,in the case of a method claim, the element is recited using the phrase“step for.”

The word “exemplary” as used herein to mean “serving as an example,instance, or illustration.” Any implementation or aspect describedherein as “exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects of the disclosure. Likewise, the term“aspects” does not require that all aspects of the disclosure includethe discussed feature, advantage or mode of operation. The term“coupled” is used herein to refer to the direct or indirect couplingbetween two objects. For example, if object A physically touches objectB, and object B touches object C, then objects A and C may still beconsidered coupled to one another—even if they do not directlyphysically touch each other. For instance, a first die may be coupled toa second die in a package even though the first die is never directlyphysically in contact with the second die.

Also, it is noted that the embodiments may be described as a processthat is depicted as a flowchart, a flow diagram, a structure diagram, ora block diagram. Although a flowchart may describe the operations as asequential process, many of the operations can be performed in parallelor concurrently. In addition, the order of the operations may berearranged. A process is terminated when its operations are completed. Aprocess may correspond to a method, a function, a procedure, asubroutine, a subprogram, etc. When a process corresponds to a function,its termination corresponds to a return of the function to the callingfunction or the main function.

The various features of the invention described herein can beimplemented in different systems without departing from the invention.It should be noted that the foregoing aspects of the disclosure aremerely examples and are not to be construed as limiting the invention.The description of the aspects of the present disclosure is intended tobe illustrative, and not to limit the scope of the claims. As such, thepresent teachings can be readily applied to other types of apparatusesand many alternatives, modifications, and variations will be apparent tothose skilled in the art.

What is claimed is:
 1. A method of calibrating sensors, comprising:receiving temperature measurements captured by a plurality of sensorsdeployed within a temperature-controlled asset, the plurality of sensorsincluding a first sensor that has been calibrated with respect to theplurality of sensors during an initial calibration of the plurality ofsensors; and continuously recalibrating the plurality of sensors basedon the temperature measurements, including recalibrating the firstsensor when a difference is identified between temperature measurementsin a time series of sensor data received from the first sensor and atleast one other time series of sensor data received from the pluralityof sensors.
 2. The method of claim 1, further comprising: determining aloss of calibration or accuracy of the first sensor based on adetermination that the time series of sensor data received from thefirst sensor has lost correlation or covariance with one or more timeseries of sensor data received from its peers in the plurality ofsensors.
 3. The method of claim 1, further comprising: ignoringmeasurements provided by the first sensor when the time series of sensordata received from the first sensor is determined to have lostcorrelation or covariance with a time series of sensor data receivedfrom a plurality of peer sensors in the plurality of sensors.
 4. Themethod of claim 1, further comprising: substituting measurementsprovided by the first sensor with estimated measurements generated forthe first sensor by a plurality of peer sensors when the time series ofsensor data received from the first sensor is determined to have lostcorrelation or covariance with time series of sensor data received fromthe plurality of peer sensors.
 5. The method of claim 1, wherein theinitial calibration is performed using a standards-certifiedthermometric device.
 6. The method of claim 1, wherein the first sensoris recalibrated when differences between temperature measurements in thetime series of sensor data received from the first sensor andtemperature measurements in the at least one other time series of sensordata indicate a loss of sensitivity of the first sensor.
 7. The methodof claim 1, wherein the initial calibration is performed when a chamberof the temperature-controlled asset is empty.
 8. The method of claim 7,wherein the initial calibration is performed when temperature within thechamber is stable.
 9. The method of claim 7, wherein the initialcalibration is performed while temperature measurements captured by twoor more of the plurality of sensors are unchanging.
 10. The method ofclaim 7, wherein the initial calibration is performed when temperaturewithin the chamber varies over time.
 11. The method of claim 10, whereinthe initial calibration is performed using lag-adjusted temperaturesprovided by two or more of the plurality of sensors.
 12. The method ofclaim 1, further comprising: detecting a change in conditions within achamber of the temperature-controlled asset based on differences intemperature measurements between multiple pairs of sensors in theplurality of sensors; and recalibrating the plurality of sensors toaccount for the change in conditions within the chamber.
 13. The methodof claim 12, wherein the change in conditions within the chambercomprises an addition, relocation or removal of an object within thechamber.
 14. The method of claim 1, wherein the first sensor issandwiched between two laminating sheets to form a sensing device thatis attached to a surface within a chamber of the temperature-controlledasset.
 15. The method of claim 1, two or more of the plurality ofsensors are sandwiched between two laminating sheets in a desiredconfiguration to form a sensing device that is attached to a shelfwithin a chamber of the temperature-controlled asset.
 16. The method ofclaim 1, two or more of the plurality of sensors are sandwiched betweentwo laminating sheets in a desired configuration to form a sensingdevice that is attached to a surface within a chamber of thetemperature-controlled asset.
 17. A temperature sensing device,comprising: a first sensor attached to a first layer of the temperaturesensing device, wherein the first sensor is one of a plurality ofsensors deployed within a temperature-controlled asset, wherein thefirst sensor is calibrated with respect to other sensors in theplurality of sensors during an initial calibration of the plurality ofsensors, wherein the plurality of sensors is continuously recalibratedbased on temperature measurements provided by the plurality of sensors,and wherein the first sensor is recalibrated when a difference isidentified between temperature measurements in a time series of sensordata received from the first sensor and at least one other time seriesof sensor data received from the plurality of sensors.
 18. Thetemperature sensing device of claim 17, further comprising a secondlayer attached to the first layer, wherein the first sensor is providedbetween the first layer and the second layer, and wherein thetemperature sensing device is attached to or embedded in a surfacewithin a chamber of the temperature-controlled asset.
 19. Thetemperature sensing device of claim 17, further comprising a secondsensor attached to a first layer of the temperature sensing device,wherein the second sensor is included in the plurality of sensorsdeployed within the temperature-controlled asset.
 20. Aprocessor-readable storage medium configured with code that, whenexecuted by a processor, causes the processor to: receive temperaturemeasurements captured by a plurality of sensors deployed within atemperature-controlled asset, the plurality of sensors including a firstsensor that has been calibrated with respect to the plurality of sensorsduring an initial calibration of the plurality of sensors; andcontinuously recalibrate the plurality of sensors based on thetemperature measurements, wherein the first sensor is recalibrated whena difference is identified between temperature measurements in a timeseries of sensor data received from the first sensor and at least oneother time series of sensor data received from the plurality of sensors.